Automating Materials Science: Advanced Data Extraction Techniques for Research and Drug Development

Abstract

This article provides a comprehensive overview of modern data extraction methodologies applied to materials science literature, addressing the critical need for automated, large-scale data collection to fuel informatics and discovery. We explore the foundational shift from manual extraction to AI-driven approaches using Large Language Models (LLMs) and specialized Natural Language Processing (NLP) models like MaterialsBERT. The scope encompasses practical methodologies, from building extraction pipelines to optimizing performance and cost. We also address significant challenges including data quality, model hallucination, and integration into existing research workflows, and conclude with a comparative analysis of different extraction frameworks and their validation for generating reliable, research-ready databases.

The Data Imperative: Why Automated Extraction is Revolutionizing Materials Science

The rapid expansion of materials science literature has created a vast repository of knowledge, most of which is locked in unstructured formats like PDFs. This poses a significant bottleneck for data-driven research and materials discovery. Automated information extraction (IE) has emerged as a critical field to transform this unstructured text and tabular data into structured, machine-readable databases, thereby accelerating the development of new materials [1] [2]. In materials science, this task is uniquely complex, requiring the accurate capture of the "materials tetrahedron"—the intricate relationships between a material's composition, structure, properties, and processing conditions [3]. This document outlines the specific challenges, quantifies the performance of current extraction methodologies, and provides detailed application protocols for researchers embarking on data curation projects.

The Materials Science Data Landscape

Information in materials science literature is distributed across both text and tables, each presenting distinct extraction challenges. A manual analysis of papers reveals that different types of data favor different formats, as shown in the table below.

Table 1: Prevalence of Key Data Entities in Materials Science Papers [3]

Data Entity	Reported in Text	Reported in Tables
Compositions	78%	74%
Properties	Information Missing	82%
Processing Conditions	86%	18%
Testing Methods	84%	16%
Precursors (Raw Materials)	80%	20%

Note: Percentages exceed 100% because the same information can be reported in both text and tables.

A critical finding is that while compositions are frequently mentioned in text, 85.92% of them are actually housed within tables, which are often the primary source for structured data [3]. This underscores the necessity of developing robust methods for parsing tabular data.

Quantified Challenges in Information Extraction

Challenges in Extracting Data from Tables

Tabular data, while structured in appearance, lacks standardization, making automated extraction difficult. The table below categorizes and quantifies these challenges based on an analysis of 100 composition tables.

Table 2: Key Challenges in Extracting Compositions from Tables [3]

Challenge Category	Specific Challenge	Frequency of Occurrence	Impact on Extraction (F1 Score)
Table Structure	Multi-Cell, Complete Info (MCC-CI)	36%	65.41%
	Single-Cell, Complete Info (SCC-CI)	30%	78.21%
	Multi-Cell, Partial Info (MCC-PI)	24%	51.66%
	Single-Cell, Partial Info (SCC-PI)	10%	47.19%
Data Provenance	Presence of Nominal & Experimental Compositions	3%	Difficult to separate correctly
	Compositions Inferred from External References	11 tables (of 100)	IE models fail if data is absent
Material Identification	Composition Inferred from Material IDs	10% of tables	Failure in 60% of these cases

Performance of Modern Extraction Tools

Recent advances in Large Language Models (LLMs) have enabled new approaches to these challenges. The following table summarizes the performance of different modern data extraction methods as reported in recent studies.

Table 3: Performance of Automated Data Extraction Methods

Method / Tool	Domain / Task	Reported Performance Metric	Score
ChatExtract (GPT-4) [4]	Bulk Modulus Data Extraction	Precision: ~91%, Recall: ~88%	90.8% Precision, 87.7% Recall
ChatExtract (GPT-4) [4]	Critical Cooling Rates (Metallic Glasses)	Precision: ~92%, Recall: ~84%	91.6% Precision, 83.6% Recall
GPT-4V (Vision) [5]	Polymer Composites - Composition	Accuracy	0.910
GPT-4V (Vision) [5]	Polymer Composites - Property Name	F₁ Score	0.863
GPT-4V (Vision) [5]	Polymer Composites - Property Details (Exact Match)	F₁ Score	0.419
DiSCoMaT (GNN) [3]	Glass Compositions from Tables (MCC-CI)	F₁ Score	65.41%

Application Notes & Experimental Protocols

Protocol A: ChatExtract for Data from Text

The ChatExtract method utilizes a conversational LLM with a series of engineered prompts to achieve high-precision data extraction from text, minimizing the model's tendency to hallucinate information [4].

Workflow Overview:

Materials and Reagents:

• Source Documents: A corpus of scientific PDFs relevant to the target material property.
• Text Pre-processing Tool: A tool like pandoc or a Python PDF library (e.g., PyMuPDF) to remove PDF/HTML/XML syntax and split text into sentences.
• Conversational LLM Access: API or web interface access to a powerful conversational LLM such as GPT-4 [4].
• Computing Environment: Standard consumer-grade hardware is sufficient for running the pipeline and managing API calls [1].

Step-by-Step Procedure:

• Text Preparation: Gather relevant papers and convert them to plain text. Divide the full text of each document into individual sentences.
• Stage A - Initial Relevancy Classification:
  • Prompt: Submit each sentence to the LLM with a prompt asking if it contains a numerical value and unit for the specific property of interest (e.g., bulk modulus, critical cooling rate).
  • Action: Filter and retain only sentences classified as "positive." This step typically reduces the dataset size by two orders of magnitude (a 1:100 ratio of relevant to irrelevant sentences) [4].
• Passage Construction:
  • For each positive sentence, create a text passage that includes the paper's title, the sentence immediately preceding the target sentence, and the target sentence itself. This provides crucial context, such as the material name [4].
• Stage B - Data Extraction:
  • Step B1: Single vs. Multiple Value Check: Submit the constructed passage to the LLM to determine if it contains a single data point or multiple data points.
  • Step B2 - Single-Value Extraction Path: If single, use direct prompts to extract the Material, Value, and Unit separately. Prompts must explicitly allow for a "Not Mentioned" response to discourage guessing [4].
  • Step B3 - Multi-Value Extraction Path: If multiple, initiate a series of follow-up prompts. This path uses uncertainty-inducing redundant questioning. For example, after an initial extraction, ask: "I think [Material X] has a value of [Value Y] [Unit Z]. Is this correct?" This forces the model to re-analyze and verify its own extraction, significantly improving accuracy [4].
• Data Output: The final extracted data should be structured into a machine-readable format, such as a CSV file, with columns for Material, Value, and Unit.

Protocol B: Multi-Modal LLM for Data from Tables

This protocol describes a method for extracting sample-level information from tables in PDFs using a multi-modal LLM, which has shown superior performance compared to text-only or OCR-based approaches [5].

Workflow Overview:

Materials and Reagents:

• Source Documents: PDFs of research papers containing tables with target data (e.g., on polymer composites).
• Table Extraction Tools:
  • Image Capture Tool: Any screenshot utility.
  • Structured Table Parser: A tool like ExtractTable (outputs CSV) [5].
  • OCR Tool: A tool like OCRSpace API (outputs unstructured text) [5].
• Multimodal LLM: Access to a vision-enabled LLM such as GPT-4 with Vision (GPT-4V) [5].
• Annotation Platform (for validation): A platform like Label Studio for creating ground-truth data, requiring two or more human annotators to ensure accuracy [5].

Step-by-Step Procedure:

• Dataset and Ground Truth Preparation:
  • Select a set of papers relevant to your subdomain (e.g., polymer composites).
  • For a subset of tables, have at least two human annotators manually extract the ground truth data. This includes identifying samples and their associated composition data (matrix, filler, fraction, surface treatment) and properties (name, value, conditions) [5].
• Table Digitization (Parallel Input Preparation):
  • Image Input (Recommended): Take a high-quality screenshot of the entire table, ensuring the caption is included. The visual format preserves structural cues [5].
  • Structured Input (CSV): Use a PDF table extraction tool like ExtractTable to convert the table into a CSV format. This explicitly encodes the table's structure [5].
  • Unstructured Input (OCR Text): Use an OCR tool on the table image to get a plain text version. This method often loses structural information [5].
• LLM Prompting and Execution:
  • Provide the LLM with detailed instructions for the Named Entity Recognition (NER) and Relation Extraction (RE) tasks. The prompt should specify the exact entities to find and how to associate them.
  • Submit the prepared inputs (image, CSV, or OCR text) to the multimodal LLM. Studies have shown that using the image input directly with GPT-4V yields the highest accuracy [5].
• Data Validation and Structuring:
  • Compare the LLM's output against the manually created ground truth.
  • Calculate performance metrics such as accuracy and F₁ score to gauge the method's effectiveness for your specific dataset.
  • Resolve any discrepancies through adjudication or model refinement.
  • Export the final, validated data into a structured database or knowledge graph.

Table 4: Key Resources for Materials Data Extraction and Management

Resource Name	Type	Function / Application
KnowMat [1]	Extraction Pipeline	An accessible, Flask-based web pipeline using lightweight open-source LLMs (e.g., Llama) to extract key materials information from text and save to CSV.
ChatExtract [4]	Extraction Methodology	A prompt-engineering protocol for conversational LLMs (e.g., GPT-4) to achieve high-precision data extraction from text with minimal upfront effort.
GPT-4 with Vision (GPT-4V) [5]	Multimodal Model	An LLM capable of processing table images directly, outperforming text-based table extraction methods in accuracy for composition and property data.
DiSCoMaT [3]	Specialized IE Model	A graph neural network-based model designed specifically for extracting material compositions from complex table structures in scientific papers.
MaterialsMine / NanoMine [5]	Data Repository & KG	A framework and knowledge graph for manually and automatically curating experimental data on polymer composites, enabling querying and analysis.
Covidence [6]	Systematic Review Tool	A software platform that facilitates dual-reviewer data extraction during systematic literature reviews, helping to manage and reduce errors.
ExtractTable [5]	Table Parser	A commercial tool for converting tabular data in PDFs into structured CSV files, providing a clean input for LLMs.

The expansion of materials informatics is fundamentally constrained by the availability of high-quality, structured data. Much of the critical information on material properties, synthesis, and performance remains locked within unstructured text, tables, and figures of research publications. Automated data extraction technologies are therefore not merely supportive tools but foundational components that power the entire materials discovery pipeline. By transforming unstructured text into computable data, these methods directly fuel the development of machine learning (ML) models and predictive informatics, enabling the accelerated design of polymers, alloys, and energetic materials [7]. This application note details the core protocols and quantitative performance of advanced data extraction techniques that are central to modern materials research and development.

Automated Data Extraction Protocols & Performance

The transition from manual data curation to automated extraction using Large Language Models (LLMs) represents a paradigm shift. The following protocols and their associated performance metrics demonstrate the viability of these methods for building reliable materials databases.

ChatExtract Protocol for Material Property Triplets

The ChatExtract framework is a state-of-the-art method designed to accurately extract material property triplets—(Material, Value, Unit)—from scientific text using conversational LLMs in a zero-shot setting, requiring no prior model fine-tuning [4].

Experimental Workflow:

• Data Preparation: Research papers are gathered and parsed to remove HTML/XML syntax. The text is then segmented into individual sentences.
• Stage A: Initial Relevancy Classification:
  • A prompt is applied to every sentence to classify it as relevant or irrelevant for containing the target property data (value and units). This step filters out the vast majority (~99%) of sentences [4].
  • The text passage for analysis is expanded to include three key elements: the paper's title, the sentence preceding the relevant sentence, and the relevant sentence itself. This ensures the material name is captured.
• Stage B: Data Extraction & Verification: This stage uses a series of engineered prompts with specific features to ensure accuracy:
  • Feature 1: Single vs. Multi-Valued Text Separation: The LLM first determines if the text contains a single data point or multiple values. Separate extraction paths are used for each, as multi-valued sentences are more prone to errors.
  • Feature 2: Explicit Allowance for Missing Data: Prompts explicitly allow for "Not mentioned" responses to discourage the LLM from hallucinating data.
  • Feature 3: Uncertainty-Inducing Redundant Prompts: Follow-up questions are designed to introduce doubt, prompting the model to re-analyze the text instead of reinforcing a previous incorrect answer.
  • Feature 4: Conversational Information Retention: All prompts are embedded in a single conversation, with the full text passage reiterated each time to leverage the model's context retention.
  • Feature 5: Strict Yes/No Format: Verification questions are constrained to Yes/No answers to simplify automated processing.

The entire ChatExtract workflow is illustrated in Figure 1 below.

Table 1: Quantitative Performance of the ChatExtract Method [4]

Material Property	Test Dataset Description	Precision (%)	Recall (%)
Bulk Modulus	Constrained test dataset	90.8	87.7
Critical Cooling Rates	Metallic glasses (full database construction)	91.6	83.6

LLM Framework for Processing-Structure-Property Relationship Extraction

Beyond simple property triplets, a more comprehensive LLM framework has been developed to extract complex Processing-Mechanism-Structure-Mechanism-Property (P-M-S-M-P) relationships, particularly from metallurgy literature [8]. This approach systematically maps the causal links that define a materials system.

Experimental Protocol:

• Multi-Stage Prompting: The framework employs a sequence of specialized prompts to deconstruct the text:
  • Prompt 1: Identify and list all key entities: properties, microstructures, processing methods, and mechanisms.
  • Prompt 2: Label the source of each piece of information (e.g., "the authors state," "it can be inferred").
  • Prompt 3: Integrate the entities into a coherent P-M-S-M-P network, establishing the directional links between them.
• Chart Generation & Refinement: The extracted network is processed into a structured data format (e.g., JSON) and then refined into a human- and machine-readable visual chart for easy interpretation.

Table 2: Performance Metrics of the P-M-S-M-P Extraction Framework [8]

Extraction Task	Accuracy / Performance Metric
Mechanism Extraction	94% Accuracy
Information Source Labeling	87% Accuracy
Human-Machine Readability Index (for Processing, Structure, Property entities)	97%

The logical flow of the P-M-S-M-P relationship extraction process is shown in Figure 2.

The Scientist's Toolkit: Research Reagent Solutions

The following table details the essential "research reagents"—the key software, data, and model components—required to implement the automated data extraction workflows described in this note.

Table 3: Essential Tools for Automated Materials Data Extraction

Item / Solution	Function & Application	Example Implementations / Sources
Conversational LLM	Core engine for zero-shot text classification, information extraction, and relationship mapping. Powers the ChatExtract and P-M-S-M-P protocols.	GPT-4, other advanced conversational models [4] [8]
Engineered Prompts	Pre-defined, optimized instructions that guide the LLM to perform specific tasks without additional training. Critical for achieving high accuracy.	Relevancy classifiers, single/multi-value discriminators, verification prompts [4]
Text Pre-processing Pipeline	Prepares raw document data for LLM analysis by handling format stripping, tokenization, and sentence segmentation.	Custom Python scripts for removing XML/HTML and sentence splitting [4]
P-M-S-M-P Framework	A structured schema for representing complex, causal materials knowledge, enabling systematic extraction beyond simple properties.	Defined ontology for metallurgy and materials science [8]
Scientific Literature Corpus	The source data; a collection of research papers (PDFs or plain text) from which material data and relationships are to be extracted.	Publisher websites, institutional repositories, PubMed Central, etc.
Mouse TREM-1 SCHOOL peptide, control	Mouse TREM-1 SCHOOL peptide, control, MF:C42H69N9O12, MW:892.0 g/mol	Chemical Reagent
Tnik-IN-6	Tnik-IN-6, MF:C13H8BrFN4, MW:319.13 g/mol	Chemical Reagent

The field of materials science generates a vast amount of knowledge, yet a critical bottleneck exists: most of this knowledge remains locked within unstructured text in millions of scientific papers. This creates a significant hurdle for data-driven discovery. The traditional process of manual data extraction is notoriously time-consuming and limits the scale of analysis. Natural Language Processing (NLP), particularly through Named Entity Recognition (NER) and Large Language Models (LLMs), is revolutionizing this landscape by enabling the automated, large-scale transformation of unstructured text into structured, actionable databases. This document outlines the core concepts and provides practical protocols for applying these advanced techniques to materials science documents, framing them within the context of an automated data extraction pipeline for research.

Core Conceptual Frameworks

The Evolution of Natural Language Processing (NLP)

NLP aims to enable computers to understand and generate human language. Its development in materials science has progressed through distinct stages:

• Handcrafted Rules (1950s-): Early systems relied on expert-defined rules, which were rigid and could only solve narrow problems [9].
• Machine Learning (Late 1980s-): Algorithms began learning from annotated text corpora, but required manual feature engineering and faced the "curse of dimensionality" [9].
• Deep Learning (Present): Neural networks like BiLSTM and the Transformer architecture automated feature engineering. This era is defined by the rise of LLMs, which possess remarkable general language abilities [9].

Named Entity Recognition (NER)

NER is a fundamental NLP task that involves identifying and classifying key information (entities) in text into predefined categories. In materials science, this typically includes:

• Inorganic material mentions
• Sample descriptors
• Phase labels
• Material properties and applications
• Synthesis and characterization methods [10]

For example, in the sentence "The synthesized CoFe2O4 nanoparticles exhibited a saturation magnetization of 80 emu/g," a NER model would identify "CoFe2O4" as a material, "nanoparticles" as a sample descriptor, and "saturation magnetization of 80 emu/g" as a property-value-unit triplet.

Large Language Models (LLMs)

LLMs are deep learning models trained on immense volumes of text. Their core working principle is token prediction: given a sequence of input tokens (sub-word units), they predict the most probable subsequent tokens [11]. Trained on diverse knowledge areas, they develop a powerful ability to understand context and generate coherent text. For materials science, this means they can interpret complex chemistry language and textual context with a flexibility that rigid, rule-based systems lack [12]. Two key paradigms for using LLMs are:

• Prompt Engineering: Crafting input instructions to guide the model to perform a specific task without additional training (zero-shot or few-shot learning).
• Fine-Tuning: Further training a pre-trained LLM on a specialized dataset (e.g., materials science text) to enhance its performance on domain-specific tasks.

Quantitative Performance of NLP Techniques in Materials Science

Table 1: Performance Benchmarks of Different Data Extraction Methods on Materials Science Texts.

Method / Model	Task Description	Reported Performance Metric	Score	Key Advantage
Traditional NER [10]	Entity recognition from abstracts	F1-score	87%	Establishes baseline for automated extraction
ChatExtract (GPT-4) [4]	Extraction of Material-Value-Unit triplets	Precision	90.8%	Minimal initial effort; high accuracy
		Recall	87.7%
ChatExtract (GPT-4) [4]	Critical cooling rates for metallic glasses	Precision	91.6%	Effective for practical database construction
		Recall	83.6%
Open-source LLMs (Qwen3, GLM-4.5) [12]	Extraction of synthesis conditions	Accuracy	>90%	Transparency; cost-effectiveness; data privacy
Fine-tuned LLM [12]	Prediction of MOF synthesis routes	Accuracy	91.0%	Demonstrates predictive capability beyond extraction
Fine-tuned LLM [12]	Prediction of synthesisability (generalisation)	Accuracy	97.8%	Strong generalisation beyond training data scope

Application Notes and Experimental Protocols

Protocol 1: Automated Data Extraction Using a Conversational LLM (ChatExtract)

ChatExtract is a method that uses advanced conversational LLMs with sophisticated prompt engineering to achieve high-quality data extraction with minimal upfront effort [4].

Workflow Overview:

Detailed Methodology:

• Data Preparation and Pre-processing

  • Input: Gather target scientific papers, typically in PDF or XML/HTML format.
  • Text Cleanup: Remove all XML/HTML tags and other non-textual syntax.
  • Sentence Segmentation: Split the clean text into individual sentences. This is a standard step in any data extraction pipeline [4].

• Stage (A): Initial Relevancy Classification

  • Objective: Filter out sentences that do not contain the target data (e.g., Material-Value-Unit triplets), drastically reducing the number of sentences for costly detailed analysis.
  • Prompt Engineering: Apply a simple prompt to every sentence to classify it as "relevant" or "irrelevant." In papers pre-filtered by a keyword search, the ratio of relevant to irrelevant sentences can be as high as 1:100, making this step crucial for efficiency [4].
  • Context Expansion: For each sentence classified as relevant, create a text passage comprising the paper's title, the preceding sentence, and the target sentence itself. This often captures the material name and improves context.

• Stage (B): Data Extraction and Verification

  • Single-Value vs. Multi-Value Path: The first prompt in this stage determines if the text passage contains a single data point or multiple data points. This is critical because multi-valued texts are more prone to extraction errors.
  • For Single-Value Texts: Use direct prompts to ask separately for the material name, value, and unit. The prompt must explicitly allow for a negative answer (e.g., "not mentioned") to discourage the model from hallucinating data [4].
  • For Multi-Value Texts: Employ a rigorous series of follow-up prompts. This strategy includes:
    • Uncertainty-Inducing Prompts: Phrasing questions to suggest the model's previous answer might be wrong, encouraging re-analysis instead of confirmation bias.
    • Redundant Verification: Asking the same question in different ways to cross-verify the extracted data.
    • Strict Yes/No Format: Enforcing a strict format for answers to reduce ambiguity and simplify automated parsing [4].
  • Output Structuring: The final prompts encourage the model to output the data in a structured format (e.g., JSON) for easy conversion into a database.

Protocol 2: Fine-Tuning an LLM for Domain-Specific Prediction

This protocol describes how to adapt a general-purpose LLM for specialized tasks, such as predicting material properties or synthesis conditions.

Workflow Overview:

Detailed Methodology:

• Dataset Curation

  • Objective: Create a high-quality dataset of input-output pairs relevant to the target task.
  • Procedure: Using the L2M3 project as an example [12]:
    • Input (X): Collect textual descriptions of MOF precursors or rich natural language descriptions containing composition and structural features (e.g., node connectivity, topology).
    • Output (Y): Associate these inputs with the corresponding synthesis conditions or property values (e.g., hydrogen storage performance).
    • Data Splitting: Split the dataset into training and validation sets (e.g., 85%/15%) for model development and evaluation [12].

• Fine-Tuning Execution

  • Model Selection: Choose a suitable pre-trained open-source model (e.g., from the Llama, Qwen, or GLM families).
  • Efficient Fine-Tuning: Use parameter-efficient methods like Low-Rank Adaptation (LoRA). LoRA freezes the pre-trained model weights and injects trainable rank-decomposition matrices into the transformer layers, significantly reducing the number of trainable parameters and computational cost [12].
  • Hardware Configuration: The required computational resources depend on the model size. For example, fine-tuning a large model like GLM-4.5-Air may require four AMD Instinct MI250X accelerators, which can be reduced to two with 4-bit quantization at a minor cost to accuracy [12].

• Validation and Deployment

  • Performance Benchmarking: Evaluate the fine-tuned model on the held-out validation set. Metrics like accuracy or similarity scores are used to compare its performance against baselines, such as closed-source models like GPT-4o [12].
  • Application: Deploy the validated model as a "recommender" tool within a research pipeline to suggest synthesis parameters for new precursors or predict properties of unreported materials.

Table 2: Essential Tools, Models, and Datasets for Materials Science Data Extraction Research.

Name	Type	Primary Function	Key Feature / Application
MatNexus [13]	Software Suite	Automated collection, processing, and analysis of scientific articles.	Generates ML-ready vector representations and visualizations for materials exploration.
MatSci-NLP [14]	Benchmark Dataset	Standardized evaluation of NLP models on materials science tasks.	First comprehensive benchmark; covers property prediction, information extraction, etc.
HoneyBee [14]	Domain-Specific LLM	A large language model fine-tuned for materials science.	Achieves state-of-the-art performance on MatSci-NLP; uses automated instruction tuning.
MOF-ChemUnity [12]	Extraction Pipeline	Information extraction for Metal-Organic Frameworks.	Links material names to co-reference names and crystal structures, forming a knowledge graph.
ChatExtract [4]	Extraction Method	A workflow for accurate data extraction using conversational LLMs.	Requires minimal initial setup; achieves >90% precision/recall with GPT-4.
Open-source LLMs (Qwen, GLM) [12]	Foundational Models	General-purpose and fine-tunable models for various tasks.	Commercially competitive; offer transparency, cost-control, and data privacy.
L2M3 [12]	Recommender System	Predicts synthesis conditions based on provided precursors.	Demonstrates the predictive power of fine-tuned LLMs within the materials domain.

Advanced Applications and Future Directions

• Hypothesis Generation: LLMs can move beyond data extraction to generate novel, synergistic materials design hypotheses by integrating scientific principles from diverse sources. For instance, they can propose new high-entropy alloys with superior cryogenic properties or solid electrolytes with enhanced ionic conductivity, ideas that have been validated by subsequent high-impact publications [15].

• Multi-Agent Systems: LLMs are increasingly deployed as the central "brain" in autonomous research systems. These LLM agents can plan multi-step procedures, interface with computational tools (e.g., simulation software), and even operate robotic platforms in self-driving labs, closing the loop from hypothesis to experimental validation [12] [16].

• Multimodal Data Extraction: Advanced pipelines now use multimodal LLMs that can interpret both text and images. For example, the "ReactionSeek" workflow directly interprets reaction scheme images from publications to extract synthetic pathways, achieving high accuracy and broadening the scope of accessible data [12].

The vast body of knowledge in materials science is embedded within unstructured scientific literature. A significant portion of this knowledge can be structured as simple triplets: a Material, a Property, and a Value [4]. The systematic extraction of these (Material, Property, Value) triplets from research papers is a fundamental step in building structured databases that enable large-scale, data-driven research and the development of predictive models [17]. This process transforms isolated facts reported in text into a structured, computable format, forming the backbone of modern materials informatics.

Traditionally, the extraction of this data has relied on manual curation or partial automation requiring significant domain expertise and upfront effort. The emergence of advanced Large Language Models (LLMs) represents a paradigm shift, offering a pathway to automate this extraction with high accuracy and minimal initial setup [4] [17]. This document outlines the core concepts of these data triplets and provides a detailed protocol for their automated extraction using state-of-the-art conversational LLMs.

Core Concepts and Definitions

The (Material, Property, Value) triplet is a concise representation of a single quantitative material characteristic.

• Material: The substance or compound under investigation. This can range from a simple element (e.g., "steel") to a complex, multi-component system (e.g., "high-entropy alloy AlCoCrFeNi").
• Property: The specific, measurable attribute of the material being reported. Examples include "bulk modulus," "yield strength," "critical cooling rate," and "band gap."
• Value: The numerical measurement of the property, accompanied by its relevant Unit (e.g., "156 GPa," "83.6 %," "4.5 K"). The unit is an indispensable component of a complete data point.

Table of Common Data Triplets in Materials Science

The following table summarizes exemplary triplets to illustrate the concept.

Material	Property	Value & Unit
Metallic Glass	Critical cooling rate	87.7 K/s
High-Entropy Alloy	Yield strength	1.31 GPa
Gorilla (Older)	Chest-beating rate	0.91 beats per 10 h [18]
Gorilla (Younger)	Chest-beating rate	2.22 beats per 10 h [18]

Experimental Protocol: Automated Triplet Extraction with ChatExtract

The ChatExtract method is a fully automated, zero-shot approach for extracting (Material, Property, Value) triplets from research papers using conversational LLMs and prompt engineering. It achieves high precision and recall (both close to 90% with models like GPT-4) by leveraging a structured conversational workflow to minimize hallucinations and extraction errors [4].

Research Reagent Solutions

This protocol relies on the following key components:

Item	Function in Protocol
Conversational LLM (e.g., GPT-4)	The core engine for natural language understanding and data extraction. Its information retention across a conversation is critical.
Set of Engineered Prompts	Pre-defined, sequential instructions that guide the LLM through identification, extraction, and verification steps.
Python Runtime Environment	For executing the automated workflow, handling API calls to the LLM, and processing input/output texts.
Corpus of Research Papers	Input data; PDFs converted to plain text and segmented into sentences or short passages.

Step-by-Step Workflow

The ChatExtract method consists of two main stages. The following diagram outlines the complete, automated workflow.

Stage A: Initial Relevancy Classification

• Input Preparation: Gather research papers and convert them to plain text, removing any XML/HTML syntax. Segment the text into individual sentences [4].
• Relevancy Filtering: Apply a simple prompt to all sentences to classify them as relevant or irrelevant. A relevant sentence is one that contains data for the property of interest (i.e., a value and its units). This step efficiently weeds out the vast majority of sentences (typically a 1:100 ratio of relevant to irrelevant) [4].
  • Example Prompt: "Does the following sentence from a materials science paper contain a numerical value and a unit for the property '[PROPERTY NAME]'? Answer only 'Yes' or 'No'. Sentence: '[SENTENCE]'"
• Text Passage Expansion: For each sentence classified as positive, create a short text passage for deeper analysis. This passage consists of three elements: the paper's title, the sentence immediately preceding the target sentence, and the target sentence itself. This expansion helps capture the material name, which is often mentioned outside the immediate target sentence [4].

Stage B: Data Extraction & Verification

This stage uses a series of engineered prompts applied within a single conversational thread with the LLM to maintain context.

• Single vs. Multiple Value Determination: The first prompt in Stage (B) asks the model to determine if the text passage contains a single data value or multiple data values. This is a critical branching point, as the extraction strategy differs for each [4].
  • Example Prompt: "Does the following text contain only one single value for the property '[PROPERTY NAME]'? Answer only 'Yes' or 'No'. Text: '[TEXT PASSAGE]'"
• Path 1: Extraction from Single-Value Texts:
  • For texts containing a single value, directly ask a series of simple, separate prompts to extract the material name, the numerical value, and the unit.
  • Key Feature: Explicitly allow for a negative answer to discourage the model from hallucinating data that isn't present [4].
  • Example Prompts:
    • "What is the numerical value for the [PROPERTY NAME] in the text? If no value is given, answer 'None'. Text: '[TEXT PASSAGE]'"
    • "What is the unit for this value? If no unit is given, answer 'None'. Text: '[TEXT PASSAGE]'"
    • "What is the name of the material this value refers to? If no material is named, answer 'None'. Text: '[TEXT PASSAGE]'"
• Path 2: Extraction from Multi-Value Texts:
  • Texts with multiple values are more prone to errors. Use a strategy of purposeful redundancy and uncertainty-inducing follow-up prompts [4].
  • First, ask the model to extract all data points.
  • Then, for each extracted piece of data, ask a follow-up verification question that suggests uncertainty, prompting the model to re-analyze the text instead of reinforcing a previous error.
  • Example Verification Prompt: "I think the value X for material Y might be incorrect. Could you double-check if the text actually states this? Answer only 'Yes' or 'No'. Text: '[TEXT PASSAGE]'"
• Output Structuring: Enforce a strict format for the LLM's final answers (e.g., JSON) to simplify automated post-processing into a structured database [4].

Key Technical Features for Success

The high accuracy of the ChatExtract protocol is enabled by several key technical features [4]:

• Information Retention: All prompts are embedded within a single conversation, allowing the LLM to retain context from previous questions and answers.
• Uncertainty-Inducing Redundancy: The use of follow-up questions that introduce doubt forces the model to re-analyze the text, overcoming the tendency to confabulate.
• Structured Yes/No Format: Restricting answers to a binary format for verification questions reduces ambiguity and simplifies automation.
• Explicit Allowance for Missing Data: Prompting the model with options like "If not present, answer 'None'" significantly reduces hallucinations.

The (Material, Property, Value) triplet is a fundamental unit of structured knowledge in materials science. The ChatExtract protocol provides a robust, automated, and transferable method for extracting these triplets from scientific text with minimal initial effort. By leveraging advanced conversational LLMs and sophisticated prompt engineering, this approach enables the rapid construction of high-quality materials databases, accelerating the pace of data-driven materials discovery and design.

The field of materials science is experiencing a data revolution, with the overwhelming majority of materials knowledge published as peer-reviewed scientific literature. This literature contains invaluable information on material compositions, synthesis processes, properties, and performance characteristics. However, this knowledge repository exists primarily in unstructured formats, creating a significant bottleneck for large-scale analysis. The prevalent practice of manually collecting and organizing data from published literature is exceptionally time-consuming and severely limits the efficiency of large-scale data accumulation, creating an urgent need for automated materials information extraction solutions [9].

The challenge is particularly acute in materials science due to the technical specificity of the terminology and the complex, heterogeneous nature of the information presented. Recent progress in natural language processing (NLP) has provided tools for high-quality information extraction, but these tools face significant hurdles when applied to scientific text containing specific technical terminology. While substantial efforts in information retrieval have been made for biomedical publications, materials science text mining methodology is still at the dawn of its development, presenting both challenges and opportunities for researchers in the field [19].

The Technical Framework: NLP and LLMs

The Evolution of Natural Language Processing

Natural Language Processing (NLP) has evolved significantly since its inception in the 1950s, progressing through three distinct developmental stages. The field began with handcrafted rules based on expert knowledge, which could only solve specific, narrowly defined problems. The machine learning era emerged in the late 1980s, leveraging growing volumes of machine-readable data and computing resources, though it faced challenges with sparse data and the curse of dimensionality. The current deep learning era utilizes neural network architectures like bidirectional long short-term memory networks (BiLSTM) and the Transformer model, which forms the core of modern large language models [9].

The fundamental objective of NLP is to enable computers to understand and generate text through two principal tasks: Natural Language Understanding (NLU), which focuses on machine reading comprehension via syntactic and semantic analysis, and Natural Language Generation (NLG), which involves producing phrases, sentences, and paragraphs within a given context [9].

Key Technological Foundations

Several technological breakthroughs have been instrumental in advancing NLP capabilities for scientific text processing:

• Word Embeddings: These distributed representations of words enable language models to process sentences and understand underlying concepts. Word embeddings are dense, low-dimensional representations that preserve contextual word similarity, with implementations like Word2Vec and GloVe capturing latent syntactic and semantic similarities among words through global word-word co-occurrence statistics [9].

• Attention Mechanism: First introduced in 2017 as an extension to encoder-decoder models, the attention mechanism allows models to focus on relevant parts of the input sequence when processing data, significantly improving performance on complex language tasks [9].

• Transformer Architecture: This architecture, characterized by its self-attention mechanism, serves as the fundamental building block for modern large language models (LLMs) and has been employed to solve numerous problems in information extraction and code generation [9].

Large Language Models in Materials Science

The emergence of pre-trained models has ushered in a new era in NLP research and development. Large language models (LLMs) such as Generative Pre-trained Transformer (GPT), Falcon, and Bidirectional Encoder Representations from Transformers (BERT) have demonstrated general "intelligence" capabilities through large-scale data, deep neural networks, self and semi-supervised learning, and powerful hardware. In materials science, GPTs offer a novel approach to materials information extraction through prompt engineering, distinct from conventional NLP pipelines [9].

Table: Key Large Language Model Architectures Relevant to Materials Science

Model Architecture	Key Characteristics	Applications in Materials Science
Transformer	Self-attention mechanism	Fundamental building block for LLMs
BERT (Bidirectional Encoder Representations from Transformers)	Bidirectional context understanding	Information extraction from scientific text
GPT (Generative Pre-trained Transformer)	Generative capabilities	Materials information extraction via prompt engineering
Falcon	Open-source LLM	Specialized materials science applications

Quantitative Analysis of Text Mining Scale and Performance

The scale of the text processing challenge in materials science can be understood through both volume considerations and performance metrics of current extraction methodologies.

Volume and Processing Metrics

Materials informatics represents a rapidly growing field, with the revenue of firms offering MI services forecast to reach US$725 million by 2034, representing a 9.0% compound annual growth rate (CAGR). This growth is driven by increasing recognition of the value of data-centric approaches for materials research and development [20].

Table: Text Mining Performance Metrics in Scientific Literature Processing

Performance Metric	Current Capability	Target/Advanced Performance
Overall Concept Extraction Accuracy	Approximately 80% or higher in many cases [21]	Approaching individual human annotator performance [21]
Information Extraction Tasks	Named entity recognition, relationship extraction [9]	Autonomous knowledge discovery [9]
Critical Error Reduction	Identification of prevalent errors through systematic analysis [21]	Implementation of writing guidelines to minimize processing errors [21]

Experimental Protocols for Large-Scale Text Processing

Protocol: Automated Materials Data Extraction Pipeline

Objective: To automatically extract structured materials information from unstructured scientific text at scale.

Materials and Methods:

• Document Collection and Preprocessing
  • Collect target materials science publications in PDF format through API access to repositories or manual upload.
  • Convert PDF documents to plain text using high-fidelity text extraction tools.
  • Clean and normalize text through tokenization, sentence segmentation, and removal of non-content elements.

• Named Entity Recognition (NER)

  • Implement domain-specific NER models to identify materials science entities.
  • Utilize pre-trained models (BERT, SciBERT) fine-tuned on materials science corpus.
  • Apply conditional random fields (CRF) or deep learning models (BiLSTM) for sequence labeling.
  • Target entity types: material compounds, properties, synthesis processes, synthesis parameters, alloy compositions.

• Relationship Extraction

  • Apply dependency parsing to analyze grammatical structure of sentences.
  • Implement rule-based patterns for specific relationship types.
  • Utilize supervised learning models to classify relationship types between entities.
  • Extract relationships: material-property, process-parameter, composition-property.

• Knowledge Base Population

  • Map extracted entities to standardized terminologies and ontologies.
  • Resolve coreferences within and across documents.
  • Normalize numerical values and units to standard representations.
  • Store structured information in materials knowledge graph.

Validation:

• Manually annotate gold standard corpus of materials science documents.
• Calculate precision, recall, and F1-score against human annotations.
• Perform cross-validation with existing materials databases.

Protocol: LLM-Powered Information Extraction via Prompt Engineering

Objective: To leverage large language models for materials information extraction through structured prompting.

Materials and Methods:

• Prompt Design
  • Develop task-specific prompts with clear instructions and examples.
  • Incorporate domain knowledge through few-shot learning examples.
  • Structure prompts to output standardized formats (JSON, CSV).

• Model Configuration

  • Select appropriate LLM (GPT, Claude, or domain-specific models).
  • Configure model parameters (temperature, max tokens, top-p).
  • Implement error handling for API-based model access.

• Output Processing

  • Parse structured outputs from model responses.
  • Implement validation checks for extracted data.
  • Resolve inconsistencies through iterative prompting.

• Integration

  • Combine LLM extraction with traditional NLP pipelines.
  • Implement human-in-the-loop verification for critical data.
  • Establish continuous learning from correction feedback.

Visualization of Text Processing Workflows

Materials Science Text Mining Workflow

LLM-Based Extraction Pipeline

The Scientist's Toolkit: Research Reagent Solutions

Table: Essential Tools for Materials Science Text Mining

Tool Category	Specific Examples	Function in Text Processing
NLP Libraries	SpaCy, NLTK, Stanza	Provide foundational NLP capabilities including tokenization, POS tagging, and dependency parsing
Deep Learning Frameworks	PyTorch, TensorFlow, Hugging Face Transformers	Enable development and fine-tuning of neural network models for sequence labeling and text classification
Materials Ontologies	MDO (Materials Design Ontology), ChEBI, CHEMINF	Standardize terminology and enable semantic interoperability across extracted materials data
LLM Platforms	OpenAI GPT, Claude, Falcon, BERT variants	Facilitate zero-shot and few-shot information extraction through advanced language understanding
Knowledge Graph Systems	Neo4j, Amazon Neptune, Apache Jena	Store and query complex relationships between extracted materials science entities
High-Performance Computing	GPU clusters, Cloud computing platforms	Provide computational resources for training and inference with large models and datasets
BRD9 Degrader-1	BRD9 Degrader-1, MF:C49H65FN12O7S2, MW:1017.2 g/mol	Chemical Reagent
Conopeptide rho-TIA	Conopeptide rho-TIA, MF:C105H160N36O21S4, MW:2390.9 g/mol	Chemical Reagent

Best Practices for Optimized Text Processing

Writing Guidelines for Machine-Readable Research Articles

Based on comprehensive analysis of prevalent errors in automated concept extraction, researchers can enhance the machine-readability of their publications through several straightforward practices:

• Clearly associate gene and protein names with species: Automated systems for identifying genes and proteins must determine the species first. Directly stating the species significantly reduces potential for error, especially the first time a gene or protein is mentioned [21].

• Supply critical context prominently and in proximity: Like human readers, text-mining systems use surrounding context to resolve ambiguous words and phrases. Context should be provided in the abstract and preferably in the same sentence as ambiguous concept names [21].

• Define abbreviations and acronyms: All abbreviations and acronyms should be listed with the corresponding full term the first time they are used to minimize ambiguity [21].

• Refer to concepts by name: While descriptive language has value, names provide important advantages for automated tools as they have simpler structure, less variation, and are easier to match against controlled vocabularies [21].

• Use one term per concept consistently: Using multiple terms interchangeably without clear indication that they should be considered equivalent can confuse both human readers and text-mining systems [21].

Implementation Considerations for Large-Scale Processing

Successful deployment of text mining systems for materials science requires attention to several critical implementation factors:

• Domain Adaptation: Pre-trained NLP models typically perform better on general text than scientific text, necessitating domain adaptation through fine-tuning on materials science corpora [19].

• Handling of Numerical Data and Units: Materials science literature contains extensive numerical data with units, requiring specialized processing capabilities for accurate extraction and normalization [9].

• Multi-modal Integration: Modern materials research often combines textual information with images, graphs, and tables, requiring integrated approaches that can process multiple information modalities [9].

• Scalability and Performance: Processing millions of documents requires distributed computing approaches and efficient algorithms that can scale with growing literature volumes [20].

The efficient processing of millions of journal articles represents both a formidable challenge and tremendous opportunity for accelerating materials discovery. The scale of this problem necessitates automated approaches that can transform unstructured textual information into structured, computable knowledge. Current NLP technologies and emerging LLM capabilities provide powerful tools to address this challenge, though significant work remains to achieve human-level comprehension and reliability. As these technologies continue to mature and domain-specific adaptations improve performance, automated text processing will increasingly become an indispensable component of the materials research infrastructure, enabling more rapid discovery and innovation through comprehensive utilization of the collective knowledge embedded in the scientific literature.

Building Your Pipeline: From LLMs to Specialized Models for Maximum Extraction

The acceleration of materials discovery is heavily dependent on the ability to transform unstructured knowledge from scientific literature into structured, actionable data. Within this context, the selection of an appropriate natural language processing (NLP) model—whether a versatile large language model (LLM) like GPT or LlaMa, or a specialized domain-specific BERT model—becomes a critical strategic decision. This application note provides a comparative analysis of these model families, supported by quantitative benchmarks and detailed experimental protocols, to guide researchers in developing efficient data extraction pipelines for materials science documents.

Model Architectures and Characteristics

Domain-Specific BERT Models (e.g., MaterialsBERT, MatSciBERT) are transformer-based models that undergo continued pre-training on specialized scientific corpora. For instance, MatSciBERT is initialized from SciBERT and further trained on approximately 150,000 materials science papers, yielding a corpus of ~285 million words [22]. This domain-adaptive pre-training allows the model to develop expertise in materials science nomenclature and concepts.

Large Language Models (LLMs) like GPT and LlaMa represent a different approach. These are fundamentally autoregressive models trained on massive general-domain corpora through next-token prediction. Their strength lies in their ability to perform tasks through prompt-based instruction following without task-specific fine-tuning. The GPT series has evolved from GPT-3.5 to GPT-4, with the latter demonstrating significantly improved reliability, creativity, and ability to handle nuanced instructions [23].

Quantitative Performance Comparison

The table below summarizes key performance metrics from a comprehensive study that extracted polymer-property data from ~681,000 full-text articles [24]:

Table 1: Performance comparison of models on polymer data extraction tasks

Model	Model Type	Key Performance Characteristics	Computational Requirements	Primary Strengths
MaterialsBERT	Domain-specific BERT	Foundation for extracting >1M property records from polymer literature [24]	Lower computational cost for inference [24]	Superior entity recognition in materials science texts [25] [22]
GPT-3.5	Commercial LLM	Effective for data extraction with few-shot learning [24]	Significant monetary costs for API calls [24]	Strong performance without task-specific training [24]
LlaMa 2	Open-source LLM	Competitive performance in extraction tasks [24]	High energy consumption and hardware demands [24]	Transparent, customizable, no data privacy concerns [12]

Recent benchmarks on data extraction tasks for metal-organic frameworks (MOFs) have shown that open-source models like Qwen and GLM can achieve accuracies exceeding 90%, with the largest models reaching 100% on specific extraction tasks [12]. Meanwhile, domain-specific BERT models consistently demonstrate a 1-12% performance improvement over general-purpose BERT models on named entity recognition tasks in materials science [25].

Experimental Protocols for Materials Data Extraction

Two-Stage Filtering Pipeline for LLM Deployment

The following protocol outlines an optimized workflow for extracting materials property data from full-text journal articles using a combination of filtering techniques and extraction models [24]:

Step 1: Corpus Assembly and Pre-processing

• Collect full-text journal articles from authorized publishers (Elsevier, Wiley, Springer Nature, ACS, RSC)
• Identify domain-relevant documents through keyword searching (e.g., "poly" for polymer science)
• Split documents into paragraph-level text units for processing

Step 2: Heuristic Filtering

• Develop property-specific keyword lists for target properties (e.g., "glass transition temperature," "tensile strength")
• Filter paragraphs containing these property mentions or co-referents
• Approximately 11% of paragraphs typically pass this initial filter [24]

Step 3: Named Entity Recognition (NER) Filtering

• Apply a materials-aware NER model (e.g., MaterialsBERT) to identify entities
• Retain only paragraphs containing complete entity sets: material name, property name, property value, and unit
• Approximately 3% of original paragraphs typically contain extractable records [24]

Step 4: Data Extraction

• Apply selected extraction model (BERT-based or LLM) to filtered paragraphs
• For LLMs, use few-shot learning with carefully crafted examples
• Extract and structure property data into standardized format

Step 5: Validation and Data Export

• Implement consistency checks using domain knowledge
• Export structured data to databases or knowledge graphs

Workflow Visualization

Figure 1: Two-stage filtering pipeline for efficient data extraction

The Scientist's Toolkit: Essential Research Reagents

Table 2: Key resources for implementing materials science data extraction pipelines

Resource	Type	Function	Access Information
Polymer Scholar	Database	Public repository of extracted polymer-property data	Available at polymerscholar.org [24]
MatSciBERT	Pre-trained Model	Materials domain language model for NER and relation classification	HuggingFace: m3rg-iitd/matscibert [22]
MaterialsBERT	Pre-trained Model	NER model derived from PubMedBERT for materials science	Available through referenced publications [24]
Open-source LLMs (LlaMa 2/3)	Pre-trained Model	Transparent alternative to commercial LLMs	Available with appropriate licensing [12]
MOF-ChemUnity	Extraction Framework	Specialized workflow for MOF information extraction	Code repository available [12]
Leucettinib-92	Leucettinib-92, MF:C21H22N4OS, MW:378.5 g/mol	Chemical Reagent	Bench Chemicals
Alk5-IN-79	Alk5-IN-79|ALK5 Inhibitor|For Research Use	Alk5-IN-79 is a potent, selective ALK5 inhibitor. It blocks TGF-β/Smad signaling. This product is for Research Use Only and not for human or veterinary diagnostics or therapeutics.	Bench Chemicals

The choice between GPT, LlaMa, and domain-specific BERT models depends on several project-specific factors:

Select Domain-Specific BERT Models when:

• The primary task involves named entity recognition from scientific text [25] [22]
• Computational resources or API costs are a significant constraint [24]
• Maximum performance on domain-specific texts is required without extensive prompt engineering [26]

Select Commercial LLMs (GPT series) when:

• Rapid prototyping without task-specific training is preferred [11]
• The extraction task requires complex reasoning across sentences [24]
• Budget allows for API costs and the task benefits from state-of-the-art performance [23]

Select Open-source LLMs (LlaMa series) when:

• Data privacy and reproducibility are primary concerns [12]
• Customization through fine-tuning is required [12]
• The project has computational resources for local deployment [24]

For large-scale extraction projects, a hybrid approach often delivers optimal results. The two-stage filtering protocol described in this document enables researchers to leverage the precision of domain-specific BERT models for candidate identification while utilizing the robust extraction capabilities of LLMs for final processing. This approach maximizes extraction quality while controlling computational costs [24]. As the field evolves, the increasing capability of open-source models presents promising opportunities for more accessible and reproducible materials science data extraction [12].

Protocol 1: The Core ChatExtract Workflow for Automated Data Extraction

1.1 Workflow Overview The ChatExtract methodology is a fully automated, zero-shot approach for extracting materials data from research papers. It leverages advanced conversational Large Language Models (LLMs) through a series of engineered prompts to achieve high-precision data extraction with minimal initial effort and no need for model fine-tuning [4]. The workflow is designed to overcome key limitations of LLMs, such as factual inaccuracies and hallucinations, by implementing purposeful redundancy and uncertainty-inducing questioning within a single, information-retaining conversation [4].

1.2 Step-by-Step Protocol

• Step 1: Data Preparation and Preprocessing

  • Action: Gather target research papers, remove HTML/XML syntax, and divide the text into individual sentences [4].
  • Output: A clean, sentence-segmented corpus of text from the literature.

• Step 2: Initial Relevance Classification (Stage A)

  • Action: Apply a simple prompt to all sentences to classify them as "relevant" or "irrelevant." A relevant sentence is one that contains the target materials property data (a value and its unit) [4].
  • Output: A filtered list of sentences positively identified as containing data, significantly reducing the dataset for further processing.

• Step 3: Contextual Passage Assembly

  • Action: For each positively classified sentence, assemble a short text passage comprising the paper's title, the sentence preceding the target sentence, and the target sentence itself [4].
  • Rationale: This ensures the material's name, often found in the preceding sentence or title, is included for forming complete data triplets [4].

• Step 4: Data Extraction and Verification (Stage B)

  • Action 4.1: Single vs. Multi-Valued Text Classification: Use a prompt to determine if the passage contains a single data point or multiple values. This dictates the subsequent extraction path [4].
  • Action 4.2a: Extraction from Single-Valued Text: For texts with a single value, directly prompt the LLM to extract the Material, Value, and Unit separately. Prompts explicitly allow for a negative answer to discourage hallucination [4].
  • Action 4.2b: Extraction from Multi-Valued Text: For complex sentences with multiple values, employ a series of follow-up, uncertainty-inducing prompts. These prompts ask the model to re-analyze the text and verify the correctness of extracted data, ensuring accurate correspondence between materials, values, and units [4].
  • Output: Extracted data triplets (Material, Value, Unit) in a structured format.

1.3 Workflow Visualization The following diagram, generated using Graphviz, illustrates the logical flow of the ChatExtract protocol.

Table 1: Key Features of the ChatExtract Stage B Protocol [4]

Feature	Description	Purpose
Path Splitting	Separate processing for single-valued and multi-valued texts.	Optimizes accuracy by applying simpler extraction to single values and rigorous verification to complex sentences.
Explicit Negation	Prompts explicitly allow the model to answer that data is missing.	Actively discourages the model from hallucinating or inventing data to fulfill the task.
Uncertainty-Inducing Prompts	Use of follow-up questions that suggest previous answers might be incorrect.	Forces the model to re-analyze the text instead of reinforcing a previous, potentially incorrect, extraction.
Conversational Retention	All prompts are embedded within a single, continuous conversation with the LLM.	Leverages the model's inherent ability to retain information and context from earlier in the dialogue.
Structured Output	Enforcement of a strict Yes/No or predefined format for answers.	Reduces ambiguity in the model's responses and simplifies automated post-processing of the results.

Protocol 2: Experimental Validation and Performance Benchmarking

2.1 Experimental Setup for Validation To validate the ChatExtract workflow, performance metrics were obtained through tests on established materials science datasets [4]. The protocol for validation is as follows:

• Datasets: The method was tested on a constrained dataset of bulk modulus data and a practical database construction example for critical cooling rates of metallic glasses [4].
• Model: The tests were performed using advanced conversational LLMs, specifically GPT-4 [4].
• Metrics: Precision and Recall were used as the primary metrics for evaluating performance. Precision measures the percentage of correctly extracted data points out of all extracted points, while Recall measures the percentage of correctly extracted data points out of all extractable points in the text [4].

2.2 Quantitative Performance Results Table 2: ChatExtract Performance on Materials Science Data [4]

Test Dataset	Precision (%)	Recall (%)	Key Challenge Addressed
Bulk Modulus Data	90.8	87.7	Handling of a standard materials property with varied textual contexts.
Critical Cooling Rate (Metallic Glasses)	91.6	83.6	Practical application in building a specialized database from multiple papers.

2.3 Comparative Analysis Visualization The performance of ChatExtract can be contextualized by its ability to handle different data complexities. The following diagram models the relationship between data complexity and extraction accuracy.

The Scientist's Toolkit: Research Reagent Solutions

Table 3: Essential Components for Implementing ChatExtract

Item	Function in the ChatExtract Workflow
Conversational LLM (e.g., GPT-4)	The core "reagent" that performs the language understanding and reasoning. It is pre-trained and used in a zero-shot manner, eliminating the need for fine-tuning [4].
Engineered Prompt Library	A set of pre-defined, tested prompts for relevance classification, value/unit/material extraction, and verification. These are the specific "protocols" that guide the LLM [4].
Text Pre-processing Script	Software to handle the ingestion of PDFs or XML, clean the text, and perform sentence segmentation, preparing the "raw material" for analysis [4].
Python Wrapper Code	Custom code to automate the conversational interaction with the LLM API, manage the workflow logic, and parse the structured outputs into a database [4].
NoSQL Database (e.g., MongoDB)	A flexible database system recommended for storing the final structured data triplets and associated metadata, accommodating the schema-less nature of extracted data [27].

The exponential growth of scientific literature presents a formidable challenge for researchers in materials science and drug development, where critical information remains locked within unstructured text. Automated information extraction systems are essential to transform this textual data into structured, actionable knowledge. The Dual-Stage Filtering Pipeline addresses this challenge by integrating the complementary strengths of Heuristic models and Named Entity Recognition (NER) systems. This architecture is specifically designed to enhance the accuracy and efficiency of extracting complex scientific entities—such as material compositions, processing parameters, and microstructure details—from extensive document collections. By deploying a sequential filtering mechanism, the pipeline maximizes throughput while maintaining high precision, making it particularly suited for building large-scale materials databases essential for machine learning and data-driven discovery [28] [9].

In materials science, the relationship between composition, processing, microstructure, and properties is foundational. Traditional single-pass extraction methods often struggle to capture these complex, interdependent relationships accurately. The proposed dual-stage architecture systematically processes documents to first broadly identify potential entities of interest before applying more nuanced, context-aware validation. This approach significantly reduces the computational burden of applying deep, resource-intensive NER models to entire corpora while simultaneously improving the reliability of the final extracted data [28]. The integration of this pipeline into materials informatics workflows enables researchers to rapidly synthesize experimental findings across thousands of publications, accelerating the discovery and optimization of novel functional materials, including those for pharmaceutical applications [9] [29].

Pipeline Architecture and Workflow

The dual-stage filtering architecture operates through a sequential, hierarchical process designed to efficiently sift through large document sets. The workflow ensures that only the most relevant text segments undergo computationally intensive deep learning analysis, optimizing both speed and accuracy.

Stage 1: Heuristic Filtering

The first stage employs rule-based heuristic models to perform coarse-level document triage and information identification. This layer utilizes:

• Pattern Matching: Regular expressions and lexical patterns specific to materials science terminology (e.g., chemical formulas, measurement units) to identify candidate text spans.
• Syntactic Rules: Grammar-based rules that capture common construction patterns for reporting scientific data (e.g., "X was synthesized at Y°C").
• Knowledge-Based Filters: Domain-specific dictionaries and ontologies containing known material names, synthesis methods, and property descriptors [30] [31].

The heuristic stage acts as a high-recall sieve, rapidly identifying text segments containing potential entities of interest while filtering out irrelevant content. This significantly reduces the volume of text that progresses to the more computationally expensive second stage.

Stage 2: Neural NER Processing

The second stage applies sophisticated deep learning models to the candidate text segments identified in Stage 1, performing precise entity recognition and classification:

• Model Architecture: Utilizes a Bidirectional Long Short-Term Memory network with Conditional Random Fields (BiLSTM-CRF) that effectively captures contextual dependencies in scientific text [31].
• Contextual Embeddings: Incorporates pre-trained word embeddings from scientific corpora (e.g., trained on PubMed abstracts and full-text articles) to enhance domain understanding [31].
• Entity Classification: Precisely classifies and tags entities using the IOB (inside, outside, beginning) format, distinguishing entity types and boundaries with high accuracy [31].

This staged approach creates a synergistic effect where the heuristic model ensures broad coverage while the neural NER model provides precise extraction, together achieving performance superior to either method applied independently.

The following diagram illustrates the complete workflow of the dual-stage filtering pipeline:

Dual-Stage Filtering Pipeline Workflow

Experimental Protocols

Data Preparation and Annotation

Implementing the dual-stage filtering pipeline requires meticulous data preparation to ensure optimal model performance:

• Corpus Selection: Utilize established biomedical and materials science corpora including:
  • NCBI Disease Corpus: 793 PubMed abstracts with 6,892 disease mentions [31]
  • BioCreative II GM Corpus: 20,000 sentences with gene mentions [31]
  • BioCreative V CDR Corpus: 1,500 articles with 4,409 chemical and 5,818 disease annotations [31]
• Annotation Scheme: Apply IOB (Inside, Outside, Beginning) tagging format with entity-specific labels (e.g., B-CHEM, I-CHEM, B-DISEASE, I-DISEASE) [31]
• Text Preprocessing: Implement sentence segmentation, tokenization, and part-of-speech tagging using specialized scientific text processing tools [30]
• Embedding Generation: Initialize with domain-specific word embeddings pre-trained on large-scale scientific literature (e.g., 23 million PubMed abstracts) using skip-gram models with 200 dimensions and window size of 5 [31]

Model Training Protocol

The training procedure involves sequential optimization of both pipeline stages:

• Heuristic Model Development:
  • Pattern Extraction: Manually curate 500-1,000 representative sentences from target domain to identify common syntactic patterns
  • Rule Formulation: Develop context-free grammar rules for materials science expressions
  • Dictionary Construction: Compile domain terminologies from established sources (MeSH, ChEBI, Materials Project)
  • Recall Optimization: Tune heuristic parameters to achieve >95% recall on development set

• Neural NER Model Training:
  • Architecture Configuration: Implement BiLSTM-CRF with 200-dimensional token embeddings and 100-dimensional character embeddings [31]
  • Parameter Tuning: Set batch size to 20, dropout rate to 0.5, and utilize stochastic gradient descent with learning rate 0.015 [31]
  • Context Window Optimization: Experiment with n-gram window sizes (3,5,7) to capture local context [31]
  • Validation: Use 10-fold cross-validation on training corpus to prevent overfitting
  • Early Stopping: Monitor performance on development set and halt training after 5 epochs without improvement

Integration and Deployment

The final protocol involves integrating both stages into a unified pipeline:

• API Development: Create RESTful services for each pipeline stage with JSON-based communication
• Processing Orchestration: Implement workflow manager to handle document routing between stages
• Performance Benchmarking: Conduct comparative testing against single-stage baselines (BiLSTM-CRF only, heuristic only)
• Throughput Optimization: Implement batch processing and parallelization for large-scale deployment

Performance Validation and Metrics

Rigorous validation is essential to demonstrate the efficacy of the dual-stage filtering approach compared to traditional single-model extraction systems. Performance is measured using standard information extraction metrics alongside domain-specific evaluation criteria.

Quantitative Performance Metrics

The following table summarizes the key performance indicators for evaluating the pipeline's extraction accuracy:

Table 1: Performance Metrics for Information Extraction Pipelines

Metric	Dual-Stage Pipeline	Single-Stage NER Only	Heuristic Only	Evaluation Method
Precision	85.7% [31]	82.1% [31]	78.3% [31]	Exact entity match against gold standard
Recall	85.9% [31]	83.5% [31]	91.2% [31]	Complete coverage of annotated entities
F1-Score	85.7% [31]	82.8% [31]	84.2% [31]	Harmonic mean of precision and recall
Throughput	12.8 docs/sec [28]	7.2 docs/sec [28]	24.5 docs/sec [28]	Documents processed per second
Tuple Accuracy	92.3% [28]	78.6% [28]	65.2% [28]	Correct extraction of related entity groups

The dual-stage architecture demonstrates superior performance in balancing accuracy and efficiency, particularly for complex extractions involving interrelated entities (tuples). The heuristic stage's high recall ensures comprehensive candidate generation, while the neural NER stage provides precise classification, resulting in an optimal F1-score exceeding standalone approaches [31].

Materials Science Domain Performance

In specialized materials science applications, the pipeline achieves exceptional results in extracting the complete composition-processing-microstructure-property chain:

Table 2: Performance on Materials Science Extraction Tasks

Extraction Category	Feature-Level F1	Tuple-Level F1	Key Features Extracted
Composition	96.2% [28]	95.8% [28]	Chemical elements, stoichiometry, doping
Processing	95.7% [28]	94.3% [28]	Synthesis methods, temperatures, durations
Microstructure	95.0% [28]	92.4% [28]	Phase identification, grain size, morphology
Properties	96.1% [28]	95.6% [28]	Mechanical, thermal, electrical properties

The pipeline's multi-stage design proves particularly advantageous for microstructure information, which is often scattered throughout documents and referenced indirectly. The tuple-level evaluation demonstrates the architecture's capability to maintain contextual relationships between interdependent features, achieving approximately 92-96% accuracy across all materials science categories [28].

The Scientist's Toolkit

Implementing an effective dual-stage filtering pipeline requires both computational resources and domain knowledge components. The following table details the essential research reagents and computational tools for pipeline development and deployment.

Table 3: Essential Research Reagents and Computational Tools

Tool/Category	Specific Examples	Function in Pipeline	Implementation Notes
Annotation Tools	BRAT [31], Prodigy	Manual corpus annotation	Create gold-standard training data with IOB labels
NER Models	BiLSTM-CRF [31], BERT [31]	Entity recognition and classification	Pre-train on scientific corpora for domain adaptation
Word Embeddings	PubMed embeddings [31], SciBERT	Semantic representation	200-dimensional embeddings trained on 23M+ PubMed abstracts
Heuristic Resources	Materials ontologies, Regular expressions	Initial candidate generation	Domain-specific patterns for chemical formulas, units
Evaluation Frameworks	CoNLL-2003 scorer [31], seqeval	Performance measurement	Precision, recall, F1 for exact and partial matches
Processing Libraries	spaCy, NLTK	Text preprocessing	Tokenization, sentence segmentation, POS tagging
Domain Corpora	NCBI Disease [31], CDR [31]	Training and testing	1,500+ articles with chemical/disease annotations
Shp2-IN-26	Shp2-IN-26|SHP2 Inhibitor|For Research Use	Shp2-IN-26 is a potent SHP2 inhibitor for cancer research. It targets the oncogenic phosphatase to block Ras/MAPK signaling. For Research Use Only. Not for human or veterinary use.	Bench Chemicals
Glucosylceramide synthase-IN-4	Glucosylceramide synthase-IN-4, MF:C22H18F5N3O3, MW:467.4 g/mol	Chemical Reagent	Bench Chemicals

The toolkit emphasizes components that facilitate domain adaptation, as successful extraction from materials science literature requires specialized resources beyond general-purpose NLP tools. Pre-trained embeddings on scientific corpora are particularly crucial, as they capture the unique semantic relationships in technical literature, significantly improving entity recognition accuracy compared to general domain embeddings [31].

The relationship between pipeline components and performance outcomes can be visualized as follows:

Toolkit Components and Performance Relationships

In-context learning (ICL) represents a central paradigm for task adaptation in large language models (LLMs), fundamentally enabling models to adapt their behavior based on provided examples rather than undergoing resource-intensive fine-tuning of internal parameters [32]. This approach effectively leverages the "context" embedded within the model's prompt to adapt the LLM to specific downstream tasks, spanning a spectrum from zero-shot learning (where no additional examples are provided—only task descriptive instructions) to few-shot learning (where several examples are offered) [32]. The transformative impact of artificial intelligence technologies on materials science has revolutionized how researchers approach materials problems, with in-context learning emerging as a powerful technique to accelerate data extraction from scientific literature [9].

The capability for in-context learning first appeared when language models were scaled to a sufficient size [33]. In materials science, where the overwhelming majority of knowledge exists as unstructured scientific literature, manually collecting and organizing data from published literature is exceptionally time-consuming and severely limits efficiency [9]. In-context learning mitigates this challenge by enabling LLMs to perform complex information extraction tasks with minimal examples, significantly reducing the extensive annotation effort traditionally required for Named Entity Recognition (NER) models in this domain [34].

Core Principles and Methodologies

Zero-Shot Learning Fundamentals

Zero-shot learning operates by having the model leverage its pre-existing knowledge and understanding to generate responses or outputs relevant to tasks on which it was not specifically trained, based solely on the instructions given in the prompt [32]. In this paradigm, the model receives only a task description without any examples of correct performance. For instance, determining whether a specific statement about material properties represents a scientific misconception could involve a prompt structure that presents the classification task without demonstrations [32].

Recent works have shown that zero-shot learning applications using LLMs can yield reasonable results for general tasks [32]. The fundamental strength of zero-shot learning lies in its simplicity and minimal token consumption, making it particularly valuable when context length limitations are a concern or when suitable examples for demonstration are unavailable. However, performance in zero-shot settings tends to fall short on more complex tasks requiring specialized domain knowledge or multi-step reasoning processes [33].

Few-Shot Learning Methodology

Few-shot learning addresses the limitations of zero-shot approaches by providing additional domain-specific examples to enhance the LLM's understanding of the target task [32]. The model then generalizes from these examples to perform the task effectively, even with minimal training data. According to research findings, "the label space and the distribution of the input text specified by the demonstrations are both important (regardless of whether the labels are correct for individual inputs)" [33]. Furthermore, the format used plays a key role in performance, with random labels often performing much better than no labels at all [33].

Few-shot learning typically leads to better performance than zero-shot for domain-specific tasks because the model first sees good examples that help it better understand human intention and criteria for what kinds of answers are wanted [35]. However, this approach comes at the cost of more token consumption and may hit the context length limit when input and output text are long [35]. The number of demonstrations can be adjusted based on task complexity, with researchers experimenting with increasing demonstrations (e.g., 3-shot, 5-shot, 10-shot, etc.) for more difficult tasks [33].

Advanced Hybrid Approaches

A particularly powerful innovation in this space is the blended dynamic zero-shot-few-shot in-context learning approach, which combines task-specific instructions (zero-shot learning) with non-prescriptive guidance (few-shot learning) that dynamically incorporates accurately performed tasks into the model [32]. This creates a closed feedback loop that enhances both scalability and predictability. The conversational nature of this approach allows for dynamic refinement of structured information hierarchies, enabling autonomous, efficient, scalable, and accurate identification, extraction, and verification of material property data [32].

Table 1: Performance Comparison of Prompting Techniques for Materials Data Extraction

Technique	Best Use Cases	Precision Range	Recall Range	Implementation Complexity
Zero-Shot	Simple classification, general knowledge queries	Lower (relies on pretraining)	Lower	Low
Few-Shot	Domain-specific extraction, structured output generation	Medium	Medium	Medium
Dynamic Hybrid	Complex property extraction, verification-critical applications	~96% [32]	~94% [32]	High

Application Protocols for Materials Science Data Extraction

Protocol 1: Property Extraction from Scientific Text

Objective: Extract structured material property datapoint quadruplets of material, property value, original unit, and measurement method from unstructured scientific text.

Materials and Reagents:

• Source Documents: Scientific publications in PDF format from materials science journals
• LLM Access: GPT-4 or Gemini-Pro API access [32]
• Preprocessing Tools: PDF text extraction libraries (e.g., PyMuPDF, pdfplumber)
• Validation Dataset: Manually annotated material property mentions for performance evaluation

Procedure:

• Document Preprocessing: Convert PDF documents to plain text, preserving section boundaries and sentence structure.
• Sentence Segmentation: Identify and isolate data-rich sentences containing material property measurements using pattern matching and syntactic analysis.
• Prompt Construction:
  • For zero-shot component: Include explicit instructions for quadruplet extraction format
  • For few-shot component: Incorporate 3-5 representative examples of correctly formatted extractions
  • Implement dynamic example selection based on semantic similarity to target text
• LLM Inference: Submit constructed prompts to LLM with temperature setting of 0.3 for balanced creativity and consistency.
• Output Validation: Implement rule-based validation for unit consistency and value ranges.
• Structured Storage: Parse and store extracted quadruplets in structured database format.

Troubleshooting:

• If extraction quality is low, increase few-shot examples to 5-8 demonstrations
• For hallucinated values, add explicit constraints in zero-shot instruction component
• If missing valid extractions, implement iterative refinement with error feedback

Protocol 2: Knowledge Graph Construction from Tabular Data

Objective: Transform materials research tabular data into knowledge graph structures to improve data interoperability and accessibility.

Materials and Reagents:

• Source Data: Non-standardized table formats from materials research publications
• Processing Environment: Python environment with LLM integration capabilities
• Graph Database: Neo4j or similar graph database for storage
• Entity Recognition Model: Specialized NER model for materials science terminology

Procedure:

• Table Parsing: Extract tabular data from source documents using table recognition algorithms.
• Entity Recognition: Utilize LLMs with few-shot examples to identify material entities, properties, and relationships within tables.
• Relationship Extraction: Implement chain-of-thought prompting to deduce semantic relationships between identified entities.
• Graph Schema Mapping: Map extracted entities and relationships to predefined knowledge graph schema.
• Quality Assurance: Employ rule-based feedback loops to validate extractions and identify inconsistencies.
• Graph Population: Insert validated entities and relationships into graph database.
• Semantic Search Implementation: Configure graph traversal queries for materials knowledge retrieval.

The workflow for this protocol can be visualized as follows:

Protocol 3: Automated Materials Literature Review

Objective: Accelerate literature review process by automatically extracting and synthesizing materials property information from multiple research articles.

Materials and Reagents:

• Literature Corpus: Semantic Scholar Open Research Corpus or domain-specific collections [36]
• Embedding Models: Sentence-BERT or SciBERT for semantic similarity [36]
• Vector Database: ChromaDB or Pinecone for efficient embedding retrieval
• Classification Framework: Materials property taxonomy for categorization

Procedure:

• Corpus Filtering: Retrieve relevant materials science publications using domain-specific keywords.
• Text Processing: Segment documents into paragraphs or sentences for granular analysis.
• Embedding Generation: Transform text segments into vector representations using domain-adapted models.
• Relevance Filtering: Use few-shot classification to identify text segments containing property measurements.
• Property Normalization: Apply zero-shot unit conversion and value standardization.
• Relationship Modeling: Implement few-shot chain-of-thought prompting to identify composition-structure-property relationships.
• Synthesis Reporting: Generate structured summary of extracted knowledge using template-based generation.

Performance Metrics and Validation

Quantitative evaluation of in-context learning approaches for materials science data extraction demonstrates significant advantages over traditional methods. The PropertyExtractor tool, which implements a blended dynamic zero-shot-few-shot approach, achieved precision of approximately 96%, recall of 94%, and an error rate of approximately 10% on a constrained dataset of 2D material thicknesses [32]. For energy bandgap extraction, performance was even better with precision of 96.81%, recall of 94.72%, and error rate of approximately 7.95% [32].

Table 2: Quantitative Performance Metrics for Material Property Extraction

Extraction Target	Precision	Recall	F1-Score	Error Rate
2D Material Thickness	~96%	~94%	~95%	~10%
Energy Bandgap Values	96.81%	94.72%	95.21%	7.95%
Refractive Index (SciQu)	N/A	N/A	N/A	RMSE: 0.068 [36]

Comparative studies between conventional supervised NER methodologies and GPT-based approaches have demonstrated that LLMs not only excel in directly extracting relevant material properties based on limited examples but can also enhance supervised learning through data augmentation [34]. This hybrid approach mitigates the need to label large training datasets, which has traditionally been a significant barrier to developing specialized materials datasets [34].

The conceptual relationship between different in-context learning techniques and their application complexity can be visualized as follows:

The Scientist's Toolkit: Essential Research Reagents

Table 3: Key Research Reagents and Computational Tools for LLM-Based Data Extraction

Tool/Resource	Type	Function	Application Example
GPT-4/Gemini-Pro	LLM API	Core reasoning and extraction engine	Property quadruplet extraction from text [32]
SciBERT	Domain-adapted Language Model	Scientific text understanding	Materials entity recognition [36]
Semantic Scholar Corpus	Research Database	Source of scientific literature	Training data for literature mining [36]
PropertyExtractor	Specialized Framework	Structured data extraction	Automated database generation [32]
Vector Database	Retrieval Infrastructure	Semantic similarity search	Example selection for few-shot learning [36]
Rule-Based Validation	Quality System	Output verification and correction	Factual accuracy improvement [32]
Glyurallin B	Glyurallin B, CAS:199331-53-8, MF:C25H26O6, MW:422.5 g/mol	Chemical Reagent	Bench Chemicals
Cdk2-IN-27	Cdk2-IN-27, MF:C18H21N7O2, MW:367.4 g/mol	Chemical Reagent	Bench Chemicals

In-context learning represents a paradigm shift in how researchers can extract structured, actionable data from unstructured materials science literature. The power of few-shot and zero-shot prompting lies in its ability to leverage the vast knowledge encoded in large language models while requiring minimal examples and no extensive retraining. As demonstrated by tools like PropertyExtractor and frameworks for knowledge graph extraction, these approaches enable researchers with limited NLP experience to efficiently generate accurate materials property databases [32] [37].

The future of in-context learning in materials science will likely involve more sophisticated dynamic prompting systems that continuously refine their understanding through conversational interactions and multi-step reasoning chains. Combining these approaches with domain-specific knowledge graphs will further enhance the accuracy and reliability of extracted information, ultimately accelerating the discovery and development of novel materials for critical societal needs.

In the field of materials science informatics, a significant challenge is that vast amounts of crucial experimental data remain trapped in unstructured formats within published scientific literature [24]. The ability to automatically process full-text articles and perform precise paragraph-level analysis is therefore critical for building large-scale, structured databases that can accelerate materials discovery and development [24] [27]. This Application Note provides a detailed protocol for implementing a text processing pipeline that successfully extracted over one million polymer-property records from approximately 681,000 scientific articles, representing the current state-of-the-art in the field [24].

The data extraction process follows a sequential pipeline designed to maximize efficiency and accuracy while managing computational costs. The entire workflow, from raw article processing to structured data output, is visualized below.

Experimental Protocols

Corpus Assembly and Document Identification

Purpose: To gather a comprehensive collection of materials science literature and identify polymer-specific content for downstream processing.

Materials:

• Source Articles: 2.4 million materials science journal articles from 11 major publishers (Elsevier, Wiley, Springer Nature, American Chemical Society, Royal Society of Chemistry) published over the last two decades [24]
• Identification Method: Term-based search for "poly" in article titles and abstracts
• Output: 681,000 polymer-related documents identified for processing

Procedure:

• Access journal articles through authorized publisher portals and Crossref database indexing [24]
• Apply text normalization to titles and abstracts (lowercasing, punctuation removal)
• Execute keyword search algorithm to identify polymer-related documents
• Validate document relevance through random sampling (minimum 95% precision required)
• Segment identified documents into individual paragraphs (23.3 million total paragraphs)

Two-Stage Paragraph Filtering Protocol

Purpose: To efficiently identify paragraphs containing extractable property data while minimizing unnecessary processing by large language models.

Materials:

• Input Data: 23.3 million paragraphs from polymer-related articles
• Heuristic Filters: Property-specific keyword lists manually curated via literature review
• NER Model: MaterialsBERT (PubMedBERT-based named entity recognition model) [24]

Procedure: Stage 1: Heuristic Filtering

• Apply property-specific keyword matching to all paragraphs
• Flag paragraphs containing target polymer properties or co-referents
• Retain approximately 2.6 million paragraphs (~11% of total) that pass initial filtering

Stage 2: NER Filtering

• Process heuristic-filtered paragraphs through MaterialsBERT NER model
• Identify and classify named entities: material names, property names, numerical values, units
• Verify presence of complete extractable records (must contain all four entity types)
• Retain approximately 716,000 paragraphs (~3% of total) containing complete property records

Table 1: Paragraph Filtering Efficiency Metrics

Processing Stage	Paragraphs Retained	Retention Rate	Key Filtering Criteria
Initial Corpus	23,300,000	100%	All paragraphs from polymer articles
Heuristic Filtering	2,600,000	11.2%	Property-specific keyword presence
NER Filtering	716,000	3.1%	Complete entities: Material, Property, Value, Unit

Dual-Channel Data Extraction Protocol

Purpose: To extract structured polymer-property data using complementary NER and LLM approaches, enabling performance comparison and data validation.

Materials:

• NER Pipeline: MaterialsBERT model (specialized for materials science NER) [24]
• LLM Pipeline: GPT-3.5 (commercial) and LlaMa 2 (open-source) large language models [24]
• Input Data: 716,000 filtered paragraphs containing complete entity information
• Target Properties: 24 key polymer properties (thermal, optical, mechanical, permeability)

Procedure: MaterialsBERT NER Pipeline

• Load pre-trained MaterialsBERT model (PubMedBERT fine-tuned on materials science corpus) [24]
• Process filtered paragraphs through model inference
• Extract and link entities: associate material names with corresponding properties, values, and units
• Output structured records in JSON format with confidence scores

LLM Pipeline (GPT-3.5/LlaMa 2)

• Design few-shot learning prompts with task-specific examples [24]
• Configure model parameters: temperature=0.1, maxtokens=500, topp=0.9
• Execute API calls (GPT-3.5) or local inference (LlaMa 2) for each paragraph
• Parse model responses to extract structured property data
• Implement cost-optimization strategies (batch processing, caching)

Validation Steps:

• Cross-verify extracted data between NER and LLM pipelines
• Manual validation of random sample (minimum 1000 records per property)
• Resolve discrepancies through expert curation
• Calculate precision, recall, and F1 scores for each method

Table 2: Data Extraction Performance Comparison

Extraction Method	Records Extracted	Precision	Recall	Computational Cost	Best Use Cases
MaterialsBERT NER	300,000+ (from abstracts)	92%	88%	Low	High-volume entity extraction
GPT-3.5 Pipeline	1,000,000+ (from full-text)	89%	94%	High $$$	Complex relationship parsing
LlaMa 2 Pipeline	Comparable volume to GPT-3.5	87%	92%	Medium (local resources)	Open-source requirements

The Scientist's Toolkit: Research Reagent Solutions

Table 3: Essential Tools for Materials Science Text Mining

Tool/Resource	Type	Primary Function	Application Notes
MaterialsBERT	NER Model	Identify materials science entities	Fine-tuned on PubMedBERT, superior to ChemBERT/MatBERT [24]
GPT-3.5	LLM API	Relationship extraction and parsing	Optimize via few-shot learning; monitor API costs [24]
LlaMa 2	Open-source LLM	Local data extraction	Suitable for sensitive data; requires significant local resources [24]
MongoDB	Database	Store extracted structured data	Handles diverse material data formats; supports big data processing [27]
Polymer Scholar	Data Repository	Public dissemination of extracted data	Hosts >1M polymer-property records (polymerscholar.org) [24]
Text Analytics Tools (MonkeyLearn, TextRazor)	Text Processing	Sentiment analysis, classification	Support custom model development for specific needs [38]
Egfr-IN-88	Egfr-IN-88, MF:C22H18Cl2N4O2S, MW:473.4 g/mol	Chemical Reagent	Bench Chemicals
Antifungal agent 76	Antifungal agent 76, MF:C17H20O2, MW:256.34 g/mol	Chemical Reagent	Bench Chemicals

Data Management and Visualization Framework

Effective management of extracted data requires specialized frameworks designed for materials science information. The diagram below illustrates the complete data lineage tracking system.

The framework emphasizes tracking data lineage from initial synthesis through final analysis, ensuring proper metadata management and facilitating re-analysis with evolving algorithms [39]. This approach aligns with FAIR data principles (Findable, Accessible, Interoperable, and Reusable) and has successfully managed millions of materials synthesis and characterization experiments [39].

This protocol outlines a comprehensive framework for processing full-text articles and performing paragraph-level analysis specifically tailored to materials science documents. The two-stage filtering approach combined with dual-channel data extraction has proven effective at scale, processing millions of paragraphs to extract over one million polymer-property records. Implementation requires careful consideration of model selection, cost optimization, and data management strategies to build high-quality, structured databases from unstructured scientific literature. The resulting structured data, publicly available through Polymer Scholar, provides a foundation for accelerated materials discovery and informatics-driven research [24].

The exponential growth of materials science literature presents a significant challenge for researchers seeking to discern quantitative chemistry-structure-property relationships from published text [40]. The field of materials informatics suffers from a critical lack of data accessibility, with vast amounts of historical data effectively "trapped" in unstructured natural language formats within scientific journal articles [24]. This case study details the development and implementation of automated natural language processing (NLP) pipelines designed to extract structured polymer property data from a corpus of 2.4 million materials science articles, representing one of the largest-scale data extraction endeavors in polymer informatics [40] [24]. The work is situated within a broader thesis on data extraction from materials science documents, demonstrating a generalizable framework for converting unstructured scientific text into machine-actionable data to accelerate materials discovery.

Experimental Protocols & Workflow

The data extraction effort utilized a multi-stage pipeline to process millions of journal articles, involving corpus assembly, text processing, entity recognition, and relationship extraction.

Corpus Assembly and Preprocessing

A comprehensive corpus was assembled from over 2.4 million materials science journal articles published over the last two decades [24]. The articles were initially indexed via the Crossref database and subsequently downloaded through authorized access from 11 major publishers, including Elsevier, Wiley, Springer Nature, American Chemical Society, and the Royal Society of Chemistry [24]. To focus on polymer-relevant content, this corpus was filtered by searching for the term 'poly' in article titles and abstracts, identifying approximately 681,000 polymer-related documents [24]. The full texts of these articles were processed into individual paragraphs, resulting in a total of 23.3 million text units for subsequent analysis [24].

Named Entity Recognition with MaterialsBERT

A core component of the extraction pipeline relied on a specialized named entity recognition (NER) model. The researchers developed and trained MaterialsBERT, a language model based on the PubMedBERT architecture, by continuing pre-training on 2.4 million materials science abstracts [40]. This domain-specific model was fine-tuned for NER using a manually annotated dataset of 750 polymer abstracts, split into training (85%), validation (5%), and test (10%) sets [40].

The annotation ontology defined eight key entity types critical for polymer property extraction, as detailed in Table 1.

Table 1: Named Entity Recognition Ontology for Polymer Property Extraction

Entity Type	Description
POLYMER	Names of specific polymer materials [40]
POLYMER_CLASS	Classes or families of polymers [40]
PROPERTY_VALUE	Numerical value of a reported property [40]
PROPERTY_NAME	Name of the material property being reported [40]
MONOMER	Monomer constituents of polymers [40]
ORGANIC_MATERIAL	Other mentioned organic materials [40]
INORGANIC_MATERIAL	Other mentioned inorganic materials [40]
MATERIAL_AMOUNT	Quantities of materials used [40]

The NER model architecture used a BERT-based encoder to generate contextual token representations, followed by a linear layer with softmax activation to predict entity types for each input token [40]. This model achieved a high inter-annotator agreement score (Fleiss Kappa = 0.885) comparable to other literature benchmarks [40].

Large Language Model Extraction Protocol

To complement the NER approach, the researchers implemented a parallel extraction pipeline using large language models (LLMs), including both commercially available (GPT-3.5) and open-source (LlaMa 2) models [24]. The LLM protocol employed a targeted paragraph filtering system to optimize processing efficiency and cost:

• Heuristic Filtering: Each of the 23.3 million paragraphs was passed through property-specific heuristic filters designed to detect mentions of target polymer properties or their co-referents, manually curated through literature review [24]. This initial filter identified approximately 2.6 million paragraphs (~11%) as potentially relevant [24].
• NER-Based Filtering: A secondary filter applied the MaterialsBERT NER model to identify paragraphs containing complete extractable records with all necessary named entities (material name, property name, property value, and unit) [24]. This refined the dataset to approximately 716,000 paragraphs (~3%) containing verifiable property data [24].
• LLM Prompting and Extraction: The filtered paragraphs were processed through the LLMs using carefully designed prompts in a few-shot learning approach, providing the models with task-specific examples to guide the extraction of structured property records [24].

Target Properties for Extraction

The extraction pipeline targeted 24 key polymer properties selected based on their significance and utility for downstream machine learning applications, with a focus on thermal, optical, and mechanical properties critical for various application areas including dielectrics, filtration, and recyclable polymers [24]. The complete data extracted through these pipelines has been made publicly available via the Polymer Scholar website (polymerscholar.org) for the wider scientific community [24].

Data Extraction Workflow Visualization

The following diagram illustrates the complete data extraction pipeline from corpus assembly to structured data output:

Diagram 1: Polymer property data extraction workflow from 2.4 million articles.

Key Research Reagent Solutions

The following table details the essential computational tools and resources that formed the core "research reagent solutions" for this large-scale data extraction project.

Table 2: Essential Research Reagents and Computational Tools for Polymer Data Extraction

Tool/Resource	Type	Function in Protocol
MaterialsBERT [40] [24]	Domain-Specific Language Model	Primary NER model for identifying polymer entities, properties, and values in text.
GPT-3.5 [24]	Commercial Large Language Model	LLM for property extraction via few-shot learning and relationship establishment.
LlaMa 2 [24]	Open-Source Large Language Model	Alternative LLM for extraction tasks, providing cost-effective option.
Polymer Scholar Corpus [40] [24]	Data Repository	Curated collection of 2.4 million materials science articles for processing.
Prodigy Annotation Tool [40]	Data Annotation Software	Platform for manual annotation of training data for NER model development.
Heuristic Filters [24]	Rule-Based Text Filter	Initial text filtering system to identify paragraphs containing target properties.

Results and Data Analysis

The implementation of these extraction protocols yielded substantial structured data from previously unstructured scientific text, enabling quantitative analysis of polymer property relationships.

Extraction Volume and Performance

The scale of data extraction achieved through these pipelines represents a significant advancement in polymer informatics, with both NER and LLM approaches contributing to the final output, as detailed in Table 3.

Table 3: Data Extraction Volume and Performance Metrics

Extraction Metric	MaterialsBERT (Abstracts) [40]	Combined Pipeline (Full-Text) [24]
Articles Processed	~130,000 abstracts	~681,000 full-text articles
Property Records Extracted	~300,000 records	>1 million records
Unique Polymers Identified	Not specified	>106,000 polymers
Properties Targeted	General property extraction	24 specific properties
Public Availability	polymerscholar.org	polymerscholar.org

Model Performance Comparison

A comprehensive evaluation was conducted comparing the performance of different extraction models across key operational dimensions, as summarized in Table 4.

Table 4: Model Performance Comparison for Data Extraction Tasks

Performance Dimension	MaterialsBERT [24]	GPT-3.5 [24]	LlaMa 2 [24]
Quantity of Extraction	High (300K+ records from abstracts)	Very High (contributed to >1M records)	High (contributed to >1M records)
Quality of Extraction	High performance on NER tasks	High performance with hallucinations risk	Good performance with hallucinations risk
Computational Cost	Lower inference cost	Significant monetary cost	High energy consumption/carbon footprint
Processing Time	Efficient for targeted extraction	Slower due to API constraints	Variable based on implementation
Primary Strength	Precision in entity recognition	Versatility and relationship extraction	Open-source accessibility

Discussion

The successful extraction of over one million polymer property records from published literature demonstrates the feasibility of large-scale, automated data mining from scientific text. The dual-pipeline approach leveraging both specialized NER models and general-purpose LLMs provides complementary advantages: MaterialsBERT offers domain-specific precision and cost efficiency for entity recognition, while LLMs provide flexible relationship extraction capabilities without requiring extensive task-specific training data [24].

This work illuminates several critical considerations for future data extraction efforts in materials science. The application of LLMs presents particular challenges regarding computational costs, environmental impact, and the risk of hallucinated content, necessitating careful optimization of prompting strategies and output validation [24]. The two-stage filtering system implemented in this study (heuristic followed by NER filtering) proved essential for cost-effective LLM utilization by minimizing unnecessary processing of irrelevant text [24].

The publicly available Polymer Scholar database resulting from this extraction effort provides a valuable resource for the materials science community, enabling new approaches to materials discovery through data-driven analysis of literature-derived property relationships [40] [24]. This work establishes a foundation for future efforts in automated knowledge extraction from scientific literature, with potential applications spanning polymer design, synthesis optimization, and property prediction.

Navigating Practical Hurdles: Cost, Hallucination, and Data Quality

The application of Large Language Models (LLMs) to data extraction from materials science documents presents a significant opportunity for accelerating research and discovery. However, a critical vulnerability hindering their reliable deployment is the phenomenon of hallucination—the generation of plausible-sounding but factually incorrect or unfounded content [41] [42]. In scientific domains, where accuracy is paramount, such errors can compromise data integrity, lead to erroneous conclusions, and undermine trust in automated systems [43]. This document provides detailed application notes and protocols for mitigating hallucinations, specifically framed within the context of materials science data extraction. It outlines verification techniques and fact-checking methodologies designed for researchers, scientists, and drug development professionals.

Understanding and Categorizing Hallucinations

In scientific data extraction, hallucinations are not a monolithic problem. They can be categorized into two primary types, each requiring a distinct mitigation strategy [42]:

• Knowledge-based Hallucinations: These occur when an LLM generates content inconsistent with established scientific facts or data. This includes fabricating non-existent material properties, misstating numerical values (e.g., bandgap, tensile strength), or citing incorrect synthesis protocols. The root cause is often missing, outdated, or biased training data [44] [42].
• Logic-based Hallucinations: These involve errors in the reasoning process itself. The LLM might possess the correct factual components but fails to logically combine them, leading to flawed conclusions, incorrect causal relationships, or invalid inferences from experimental data [43] [42].

A 2025 evaluation of state-of-the-art models revealed that even leading models like Claude-3.7 and GPT-o1 demonstrate reasoning factual accuracy of only 81.93% and 82.57% respectively in their intermediate reasoning steps, underscoring the pervasiveness of this issue in complex tasks [43].

The table below summarizes the effectiveness of prominent hallucination mitigation techniques as reported in recent literature.

Table 1: Efficacy of Hallucination Mitigation Techniques in Scientific Data Extraction

Mitigation Technique	Reported Impact / Effectiveness	Primary Hallucination Type Addressed	Key Considerations
Retrieval-Augmented Generation (RAG)	Reduces hallucinations by 40–60% [45]; Cut GPT-4o's rate from 53% to 23% in one study [44]	Knowledge-based	Quality of retrieved documents is critical; requires trusted, domain-specific sources.
Fine-Tuning on Domain-Specific Data	Can improve domain accuracy by 20–35% [45]	Knowledge-based & Logic-based	Requires a high-quality, curated dataset; risk of overfitting.
Reasoning Enhancement (e.g., Chain of Thought)	Improved factual robustness by up to 49.90% in reasoning steps [43]	Logic-based	Increases computational cost and latency; requires careful prompt design.
Targeted Fine-Tuning on Hallucination Datasets	Dropped hallucination rates by ~90–96% without hurting quality in a NAACL 2025 study [44]	Knowledge-based & Logic-based	Relies on the creation of synthetic or expertly-curated examples of errors.
Multi-Agent Verification & Fact-Checking	Improved factual accuracy in a healthcare QA bot from 62% to 88% [45]	Knowledge-based	Can be complex to implement; leverages multi-step cross-verification.

Core Verification Protocols and Experimental Workflows

This section provides detailed, actionable protocols for implementing the most effective mitigation strategies.

Protocol 1: Implementing a Retrieval-Augmented Generation (RAG) Pipeline for Materials Data Grounding

Objective: To ground the LLM's responses in verified, external knowledge bases, thereby reducing knowledge-based hallucinations during data extraction from scientific literature.

Research Reagent Solutions: Table 2: Essential Components for a RAG Pipeline

Component	Function	Example Tools / Sources
Vector Database	Stores and enables efficient similarity search over document embeddings.	Chroma, FAISS, Pinecone
Text Embedding Model	Converts text passages into numerical vector representations.	text-embedding-3-small, BAAI/bge-small-en-v1.5
Domain-Specific Corpora	Provides the source of verified, factual information for retrieval.	Materials Project [27], Polymer Scholar [24], internal lab datasets, trusted publisher databases (Elsevier, Wiley)
Cross-Encoder Reranker	Improves retrieval quality by re-scoring top documents based on relevance to the query.	BAAI/bge-reranker-base

Methodology:

• Document Ingestion and Preprocessing: A corpus of trusted materials science documents (e.g., journal articles, datasheets) is collected. Text is split into manageable chunks (e.g., 500-1000 characters).
• Vector Embedding and Indexing: Each text chunk is converted into a vector embedding using a pre-trained model. These vectors are stored in a vector database.
• Query-Time Retrieval: When a user submits a query (e.g., "Extract the glass transition temperature of Polystyrene from this paragraph"), the query is also converted into an embedding.
• Similarity Search & Reranking: The database performs a similarity search to find the most relevant text chunks. An optional but recommended step uses a cross-encoder model to rerank the top results for higher precision.
• Augmented Generation: The retrieved chunks are injected into the LLM's prompt as context. The LLM is instructed to generate an answer based solely on the provided context.

RAG Workflow for Scientific Data Extraction

Protocol 2: Enhancing Factual Accuracy in Reasoning Steps

Objective: To reduce logic-based hallucinations by forcing the LLM to generate explicit, step-by-step reasoning traces, which can be monitored for inconsistencies.

Methodology (Based on RELIANCE Framework [43]):

• Structured Reasoning Prompting: Design prompts that mandate the LLM to decompose a complex data extraction task into sub-steps.
  • Example Prompt: "Extract all polymer-composite pairs and their reported tensile strength from the following text. Proceed step-by-step: 1) Identify all material names. 2) For each material, locate any mentions of 'tensile strength'. 3) Extract the numerical value and unit. 4) Synthesize the final structured record."
• Step-Level Fact-Checking: Implement a fact-checking classifier, trained on counterfactually augmented data, to evaluate the factual consistency of each generated reasoning step against the source text or known facts.
• Reinforcement Learning with Factuality Rewards: Use a reinforcement learning objective (e.g., Group Relative Policy Optimization - GRPO) that rewards the model not just for a correct final answer, but for factually correct intermediate steps. The reward function is multi-dimensional, balancing factuality, coherence, and structural correctness.

Reasoning Enhancement with Step-Level Verification

Protocol 3: Fine-Tuning for Domain-Specific Factual Robustness

Objective: To align a general-purpose LLM with the precise terminology and knowledge of materials science, reducing domain-specific hallucinations.

Methodology:

• Dataset Curation: Create a high-quality dataset for supervised fine-tuning (SFT). This dataset should include:
  • Expert-Curated Q&A Pairs: Questions about material properties and synthesis based on provided text passages, with verified answers.
  • Synthetic Hallucination Examples: Generate examples of common hallucination patterns (e.g., "The polymer Nylon-6 is stated to have a degradation temperature of X, but the source text says Y") and train the model to prefer the faithful output [44].
• Parameter-Efficient Fine-Tuning (PEFT): Use techniques like LoRA (Low-Rank Adaptation) to fine-tune the model efficiently without the cost of full parameter training, making the process accessible to smaller research teams.
• Validation: Evaluate the fine-tuned model on a held-out test set of materials science excerpts, measuring metrics like factual accuracy, faithfulness to the source, and reduction in hallucination rate.

Case Study: Data Extraction from Polymer Literature

A landmark study [24] demonstrates a practical application of these protocols. The researchers developed a hybrid framework to extract polymer-property data from 2.4 million full-text journal articles.

Workflow and Hybrid Approach:

• Heuristic and NER Filtering: A two-stage filter first identified polymer-relevant paragraphs (~681,000 articles) and then used a NER model (MaterialsBERT) to find paragraphs containing a material, property, value, and unit. This pre-filtering step is a cost-effective verification measure to avoid processing irrelevant text with more expensive LLMs.
• LLM for Relationship Extraction: The filtered paragraphs were then processed by GPT-3.5 to perform the complex task of establishing the relationship between entities and outputting structured data (e.g., polymer: Polymethyl methacrylate, property: Refractive Index, value: 1.49, unit: -).
• Outcome: The pipeline successfully extracted over one million property records for over 106,000 unique polymers, creating a vast, publicly available dataset on Polymer Scholar. This showcases how combining traditional NLP (NER) with modern LLMs, guided by rigorous preprocessing, can achieve high-throughput, reliable scientific data extraction.

The Scientist's Toolkit: Evaluation and Monitoring Platforms

Deploying these protocols requires continuous evaluation. The following platforms are essential tools for assessing and maintaining the factual accuracy of LLM-powered data extraction systems [46].

Table 3: LLM Evaluation Platforms for Scientific Applications

Platform	Primary Function	Key Strength for Research
Braintrust	Unified platform for evaluation, prompt management, and monitoring.	Enterprise-grade security; strong for collaborative, cross-functional teams (engineers and domain scientists).
LangSmith	Tracing and evaluation of complex LLM chains and agents.	Deep integration with the LangChain ecosystem; excellent for debugging multi-step data extraction workflows.
Langfuse	Open-source platform for monitoring and evaluating LLM applications.	Full data control and self-hosting; ideal for projects with strict data privacy requirements.
Arize Phoenix	Observability and monitoring for production LLM applications.	Strong capabilities for tracing and debugging complex RAG pipelines in real-time.
Anticancer agent 194	Anticancer agent 194, MF:C12H16ClN3O2, MW:269.73 g/mol	Chemical Reagent

The systematic extraction of data from materials science literature, such as developing databases for critical cooling rates of metallic glasses or yield strengths of high entropy alloys, is fundamental to accelerating research and development [4]. Large Language Models (LLMs) have emerged as powerful tools for automating this data extraction from vast sets of research papers. However, deploying these models introduces significant and often unpredictable computational and monetary expenses that can jeopardize research budgets. Conventional wisdom often focuses on technical optimizations like model switching, but evidence from industry practices in 2025 reveals a more complex picture. Organizations achieving dramatic cost reductions of 60-80% are doing so not primarily through technical tweaks, but by making fundamental changes to their AI usage patterns and questioning basic assumptions about when and how to use AI [47]. This document provides a structured framework, including detailed protocols and analytical tools, to help researchers and scientists optimize their LLM expenditures specifically within the context of materials science data extraction.

Quantitative Analysis of LLM Costs

Core Cost Components and Pricing Models

LLM cost structures are primarily built upon the token, the fundamental unit of text that a model processes. It is crucial to understand that tokenization methods vary between models, meaning the same sentence can yield a different token count depending on the model used [48].

Table 1: Fundamental Units of LLM Costing

Cost Component	Description	Typical Pricing Consideration
Input Tokens	Tokens contained in the prompt sent to the model (e.g., text from a research paper).	Generally less expensive than output tokens.
Output Tokens	Tokens generated by the model in its response.	Typically more expensive due to higher computational effort.
Context Window	The total number of tokens (input + output) a model can handle in a single interaction.	Larger windows allow processing of longer documents but can increase cost.

The two primary deployment models each present distinct cost structures:

• Commercial APIs (LLM-as-a-Service): This "pay-as-you-go" model, offered by providers like OpenAI, Anthropic, and Google, involves direct costs based on token consumption. It offers simplicity and scalability but can lead to unpredictable bills [48].
• Open-Source / Self-Hosted: While using an open-source model like Llama 3 seems "free," it carries substantial hidden costs, including infrastructure (expensive GPUs), maintenance, and specialized engineering talent. A minimal internal deployment can easily cost $125,000–$190,000 per year [48].

Comparative Cost Analysis of Selected LLMs

A clear comparison of provider costs is essential for initial model selection. Note that pricing is dynamic and subject to change; always consult provider websites for the latest rates.

Table 2: Sample LLM Cost Comparison (per 1 Million Tokens)

Provider / Model	Input Cost ($/1M tokens)	Output Cost ($/1M tokens)	Key Characteristics / Use Case
OpenAI GPT-4	~$10.00 - $30.00	~$30.00 - $60.00	High-performance model for complex extraction tasks.
OpenAI GPT-4o-mini	~$0.15 - $0.60	~$0.60 - $1.80	Cost-effective "right-sizing" for simpler tasks [49].
Claude 3.5 Sonnet	~$3.00 - $8.00	~$15.00 - $24.00	Balanced model for general reasoning.
Self-Hosted (e.g., Llama 3)	~$125,000+/year (TCO)	~$125,000+/year (TCO)	High fixed cost, potentially lower marginal cost at vast scale [48].

Strategic Optimization Framework

The most significant cost reductions come from operational and strategic changes, not just technical optimizations. Companies achieving 70%+ savings share three fundamental shifts in approach [47]:

• Usage Pattern Analysis Over Technical Optimization: Tracking cost per business outcome (e.g., cost per accurately extracted material property) instead of cost per API call.
• Temporal Optimization Over Model Optimization: Replacing real-time AI processing with batch processing for non-critical operations, reducing costs by 30-50% without impacting research velocity.
• Feature Value Assessment Over Technical Efficiency: Regularly auditing AI features based on usage analytics and business impact to eliminate entire categories of low-value expensive operations.

A common pitfall in optimization efforts is a lack of visibility into how AI costs correlate with user behavior and business outcomes. Token consumption analysis consistently shows that approximately 60-80% of AI costs typically come from 20-30% of use cases [47]. Most optimization efforts mistakenly focus on improving efficiency across all use cases rather than identifying which ones actually justify their costs.

Experimental Protocol for Data Extraction in Materials Science

ChatExtract Workflow for Materials Data Triplets

The following protocol, adapted from the ChatExtract method published in Nature Communications, is engineered for extracting precise (Material, Value, Unit) triplets from materials science texts with high accuracy [4]. This method uses a conversational LLM in a zero-shot fashion with a series of engineered prompts to minimize common LLM shortcomings like hallucinations and improper word relation interpretation.

Diagram 1: ChatExtract workflow for materials science data extraction.

Protocol Steps

• Data Preparation and Preprocessing:

  • Action: Gather relevant research papers (PDF, HTML, XML) and perform an initial keyword search to narrow the corpus.
  • Action: Remove HTML/XML syntax and clean the text. Divide the text of each paper into individual sentences. This step is standard for any data extraction effort [4].

• Stage (A) - Initial Relevancy Classification:

  • Action: Apply a simple prompt to every sentence to determine if it contains the target materials property data (a value and its unit).
  • Rationale: Even in pre-filtered papers, the ratio of relevant to irrelevant sentences can be as high as 1:100. This step efficiently eliminates noise and focuses computational resources [4].
  • Example Prompt Template: "Does the following sentence from a materials science research paper contain a numerical value and a unit for a material's property? Sentence: [Insert Sentence Text]. Answer only Yes or No."

• Contextual Passage Assembly:

  • Action: For each sentence classified as relevant ("positive"), assemble a text passage consisting of three elements: the paper's title, the sentence immediately preceding the positive sentence, and the positive sentence itself.
  • Rationale: The material's name is often not in the target sentence but is found in the preceding sentence or title. This expansion ensures the context needed for accurate triplet formation is present while keeping the text passage short to maximize extraction precision [4].

• Stage (B) - Data Extraction and Verification:

  • Action: The first prompt in this stage determines if the passage contains a single data value or multiple data values. This is a critical branching point, as the strategies differ.
  • a) Single-Value Data Extraction:
    • Action: Apply separate, direct prompts to ask for the material name, the numerical value, and the unit. Each prompt must explicitly allow for a negative answer (e.g., "Answer with 'Not Specified' if the information is not present in the text.") to discourage hallucination [4].
  • b) Multi-Value Data Extraction:
    • Action: This is a more complex, iterative process. After an initial extraction, apply a series of follow-up prompts that suggest uncertainty and introduce redundancy (e.g., "I think you said [Material A] has a value of [Value X]. Is that correct? Answer Yes or No.").
    • Rationale: These uncertainty-inducing, redundant prompts force the model to reanalyze the text instead of reinforcing a previous, potentially incorrect answer. This approach is key to achieving high precision and recall (close to 90% with models like GPT-4) on complex extractions [4].
  • Action: Enforce a strict Yes/No or predefined format for answers to reduce ambiguity and simplify automated post-processing of the LLM's responses into a structured database [4].

The Scientist's Toolkit: Research Reagent Solutions

Table 3: Essential Components for an LLM-Based Data Extraction Pipeline

Component / 'Reagent'	Function / Description	Exemplars / Notes
Conversational LLM (API)	The core inference engine for classification and data extraction. Provides general language ability without need for fine-tuning.	GPT-4, Claude 3.5 Sonnet. Essential for the ChatExtract zero-shot method [4].
Cost Tracking Dashboard	Provides visibility into token consumption and spending trends across different models and projects.	Binadox LLM Cost Tracker, Helicone. Critical for identifying cost drivers and setting budget alerts [49].
Price Comparison Tool	Allows for rapid comparison of the latest pricing across multiple LLM providers to inform model selection.	LLM Price Check. Used for initial research and shortlisting models based on cost-effectiveness [48].
Python Library (Token Cost)	A programmatic way to estimate the cost of API calls directly within application code.	tokencost (Python library). Enables cost estimation and logging during pipeline development [48].
Observability Platform	Provides real-time monitoring of costs, latency, and errors for production-grade applications.	Helicone, OpenRouter. Moves beyond simple cost tracking to full operational governance [48].

Visualization of LLM Cost Optimization Strategy

The following diagram synthesizes the strategic and operational concepts into a coherent decision-making workflow for researchers.

Diagram 2: Strategic workflow for LLM cost optimization in research.

Optimizing the computational and monetary expenses of LLMs for materials science data extraction requires a holistic approach that transcends mere technical tweaks. The most significant savings are realized by strategically aligning AI usage with core research outcomes, rigorously questioning the necessity of each AI operation, and implementing robust operational practices like batch processing. The ChatExtract protocol provides a proven, detailed methodology for achieving high-precision data extraction, while the strategic framework ensures this is done cost-effectively. By integrating these protocols, tools, and visualizations into their research workflows, scientists and developers can harness the power of LLMs for data-intensive tasks without surrendering to budgetary unpredictability, thereby sustaining long-term, data-driven innovation in materials science.

In the field of data-driven materials science, the exponential growth of material data has revealed significant challenges related to data veracity, integration, and standardization [27] [50]. Inconsistent data formats and non-standardized nomenclature emerge as primary obstacles that impede effective data extraction, sharing, and reuse across research initiatives [27]. The fragmented nature of materials data, often stored in non-standardized table formats or scattered across isolated documents, severely limits interoperability and accessibility [51]. This application note establishes detailed protocols for ensuring data quality, with a specific focus on strategies to overcome inconsistencies in format and nomenclature during data extraction from materials science documents.

Data Quality Dimensions and Common Issues

High-quality data must be evaluated across multiple dimensions that collectively determine its fitness for use in research and development. The table below summarizes the core data quality dimensions and their impact on materials science research.

Table 1: Key Data Quality Dimensions and Materials Science Implications

Dimension	Definition	Common Issues in Materials Science	Impact on Research
Completeness [52]	Sufficiency of minimum required information	Missing synthesis parameters or characterization data; optional fields left blank [53] [54]	Compromised machine learning models; inability to reproduce results
Accuracy [52]	Alignment with real-world values or verifiable sources	Incorrect unit conversions; measurement instrument errors [54]	Flawed scientific conclusions; failed experimental validation
Consistency [52]	Uniformity across multiple data instances	Conflicting property values for the same material in different databases [53]	Reduced trust in data; hesitancy in adoption for critical applications
Validity [52]	Conformance to required syntax and domain rules	Invalid characters in chemical formulas; values outside possible ranges [52]	System rejection during data ingestion; processing failures
Uniqueness [52]	Single recorded instance per real-world entity	Duplicate experimental entries with slight variations [53]	Skewed statistical analysis; over-representation of certain materials
Timeliness [52]	Availability when required and recency	Outdated characterization data for materials with known degradation [53]	Inability to support real-time research decisions; obsolete insights

The most prevalent data quality issues stemming from inconsistent formats and nomenclature include:

• Inconsistent Formatting: Data expressed in varying formats (e.g., dates: "June 5, 2023" vs. "6/5/2023"; units: metric vs. imperial) creates significant integration challenges [53] [54]. The consequences can be severe, as exemplified by NASA's $125 million Mars Climate Orbiter loss due to metric/imperial unit confusion [53].

• Unstructured Data: Materials science research data often exists in unstructured forms (text, images, discrete files) that lack organized structure, making it difficult to store, analyze, and extract value [27] [53].

• Cross-System Inconsistencies: Combining data from different experimental systems, laboratories, or databases frequently introduces formatting conflicts and structural mismatches [27] [54].

• Ambiguous Data: Column headers with unclear meanings, spelling errors, and deceptive formatting flaws introduce ambiguity that compromises data reliability [53].

Experimental Protocols for Data Extraction and Standardization

Protocol 1: Automated Data Collection Framework for Multi-Source Heterogeneous Data

This protocol addresses the challenge of extracting and standardizing data from diverse sources including databases, discrete files, and calculation outputs [27].

3.1.1 Research Reagent Solutions

Table 2: Essential Components for Automated Data Collection Framework

Component	Function	Implementation Example
MongoDB Database [27]	Document-oriented NoSQL database storing extracted data in BSON format	Facilitates easy customization and handles structured file content efficiently
Source Evaluation Module [27]	Determines whether data source is a database or calculation file	Routes data to appropriate extraction sub-modules based on source type
Data Identification Module [27]	Identifies target data using predefined keywords	Recognizes relevant materials science concepts and properties for extraction
Data Extraction Module [27]	Parses and extracts target data from identified sources	Handles both structured database queries and file parsing operations
Data Storage Module [27]	Transforms extracted data into unified storage format	Applies standardized schema to ensure consistency across all data sources

3.1.2 Workflow Implementation

3.1.3 Procedure

• Source Evaluation: Initiate the process by evaluating whether the data source is a structured database or calculation files [27].
• Data Identification: Apply predefined material science keywords to identify target data within the evaluated sources [27].
• Data Extraction: Execute extraction procedures appropriate to the source type:
  • For database sources: Query structured data using appropriate database commands [27].
  • For file sources: Parse discrete files to locate and extract relevant data points [27].
• Data Storage: Transform all extracted data into a unified storage format using a standardized database schema [27].
• Validation: Verify that extracted data conforms to predefined quality standards before releasing for research use.

Protocol 2: ChatExtract Method for Research Paper Data Extraction

This protocol utilizes advanced conversational Large Language Models (LLMs) with engineered prompts to accurately extract material-property data in the form of Material-Value-Unit triplets from research papers [4].

3.2.1 Research Reagent Solutions

Table 3: Essential Components for ChatExtract Methodology

Component	Function	Implementation Example
Conversational LLM [4]	Advanced language model capable of understanding and extracting information from text	GPT-4 or similar model with information retention capabilities
Engineered Prompts [4]	Precisely designed questions and instructions to guide data extraction	Purpose-built prompts for classification, extraction, and verification
Text Passage Constructor [4]	Assembles relevant text segments for analysis	Creates clusters of target sentence, preceding sentence, and paper title
Uncertainty-Inducing Prompts [4]	Follow-up questions that encourage negative answers when appropriate	Prevents hallucination by allowing model to reanalyze instead of reinforcing previous answers
Yes/No Answer Enforcement [4]	Strict formatting requirement for verification questions	Reduces uncertainty and enables easier response processing automation

3.2.2 Workflow Implementation

3.2.3 Procedure

• Text Preparation: Gather research papers and preprocess text by removing HTML/XML syntax and dividing content into individual sentences [4].
• Stage A - Initial Classification: Apply a simple relevancy prompt to all sentences to identify those containing data relevant to the target property (value and units) [4].
• Passage Construction: For sentences classified as positive, construct a text passage consisting of three elements: the paper title, the sentence preceding the positive sentence, and the positive sentence itself [4].
• Single/Multiple Value Assessment: Determine whether the text passage contains single or multiple data values, as this dictates the subsequent extraction path [4].
• Data Extraction:
  • For single-value texts: Directly prompt for value, unit, and material name with explicit allowance for negative answers [4].
  • For multi-value texts: Implement a series of follow-up prompts with uncertainty-inducing questions and redundant verification [4].
• Verification: Apply structured Yes/No questions to verify extracted data, leveraging the conversational model's information retention capabilities [4].

Protocol 3: Knowledge Graph Extraction Pipeline for Tabular Data

This protocol transforms tabular materials data into knowledge graphs, addressing the challenge of implicit relationships in traditional table formats [51].

3.3.1 Research Reagent Solutions

Table 4: Essential Components for Knowledge Graph Extraction Pipeline

Component	Function	Implementation Example
LLM Entity Recognition [51]	Identifies and classifies entities from table headers and content	Recognizes materials, properties, processes, and conditions
Relationship Extraction [51]	Infers relationships between identified entities	Connects materials to their properties and processing conditions
Graph Database [51]	Stores extracted entities and relationships in graph structure	Enables complex queries across connected materials data
User Verification Interface [51]	Graphical interface for human verification of extracted knowledge	Ensures high quality of the final knowledge graph through expert validation
Caching Strategies [51]	Stores extraction results for known table structures	Enhances cost efficiency and scalability for large datasets

3.3.2 Workflow Implementation

3.3.3 Procedure

• Input Preparation: Accept flat tables in CSV format where each row represents a data record and each column contains specific values [51].
• Node Extraction: Execute a multi-step node extraction process:
  • Assign node types to each column (e.g., matter, property, process) [51].
  • Identify attribute types for each column (e.g., Name, Value, Unit) [51].
  • Aggregate columns representing different attributes of the same node [51].
• Relationship Extraction: Infer relationships between extracted entities to build the network structure of the knowledge graph [51].
• User Verification: Present extracted entities and relationships through a graphical user interface for expert validation and correction [51].
• Graph Construction: Populate the graph database with verified entities and relationships, following a predefined data model based on materials science ontology [51].

Quantitative Performance Assessment

The effectiveness of these data quality strategies has been quantitatively demonstrated across multiple studies. The table below summarizes key performance metrics.

Table 5: Performance Metrics for Data Quality Strategies

Method	Precision	Recall	Application Context	Reference
ChatExtract with GPT-4 [4]	90.8%	87.7%	Bulk modulus data extraction	Nature Communications, 2024
ChatExtract for Metallic Glasses [4]	91.6%	83.6%	Critical cooling rate database development	Nature Communications, 2024
Automated Framework [27]	High accuracy and efficiency reported		Multi-source heterogeneous material data	Scientific Reports, 2025
Knowledge Graph Pipeline [51]	Successfully processes 4-90 column tables		Transformation of R&D tables to knowledge graphs	Digital Discovery, 2025

The protocols outlined in this application note provide comprehensive strategies for addressing the critical challenge of inconsistent formats and nomenclature in materials science data extraction. The ChatExtract method demonstrates that conversational LLMs with engineered prompts can achieve precision and recall rates approaching 90% for data extraction from research papers [4]. The automated framework for multi-source heterogeneous data enables standardized extraction and storage, facilitating data fusion from diverse origins [27]. The knowledge graph pipeline addresses the critical need for explicit relationships in materials data, transforming implicit table information into explicitly connected knowledge structures [51]. Collectively, these approaches provide researchers with validated methodologies to significantly enhance data quality, thereby supporting more reliable data-driven materials discovery and innovation.

Within the context of data extraction from materials science documents, polymer science presents a unique set of challenges. The field is characterized by a vast and often inconsistent lexicon of polymer acronyms and historical terminology that has evolved over decades. This application note provides a structured framework and detailed protocols for accurately identifying, resolving, and extracting information related to polymer names. This process is critical for building robust databases, enabling effective literature mining, and ensuring clear communication in research and development, including applications in drug delivery systems and medical device development [55] [56].

The core challenge stems from the co-existence of multiple naming conventions. Source-based names (e.g., polystyrene from the monomer styrene) and structure-based names (systematic IUPAC names) often run in parallel with a plethora of common abbreviations (e.g., PS, PE, PVC) [57]. Furthermore, historical terms and trade names are frequently used in literature, complicating automated data extraction. This document outlines practical methodologies to overcome these hurdles.

Historical Context and Nomenclature Standards

Understanding the evolution of polymer terminology is essential for interpreting historical literature. The molecular nature of polymers was firmly established through the work of Hermann Staudinger in the 1920s, for which he received the Nobel Prize in 1953 [56]. This was a pivotal moment that moved the field beyond the earlier "association theory" which considered polymers as colloids.

Systematic efforts to standardize nomenclature began with the formation of IUPAC bodies, such as the Sub-commission on Nomenclature in the mid-20th century [55]. Key milestones include the foundational 1952 report, which systematized naming and introduced practices like using parentheses in source-based names for multi-word monomers [55]. Subsequent work by the Commission on Macromolecular Nomenclature (established in 1968) led to the development of structure-based nomenclature, which became the standard for major indices and journals [55] [57].

A critical distinction in modern polymer science is between a polymer, defined as a substance composed of macromolecules, and a macromolecule itself, which is a single molecule characterized by the multiple repetition of constitutional units [57]. The 1996 IUPAC "Glossary of Basic Terms in Polymer Science" solidified these and other key definitions, providing the foundation for clear communication [57].

Comprehensive Polymer Acronyms and Definitions

The table below summarizes common polymer acronyms and their full chemical names, serving as a key reference for data extraction and annotation. This list consolidates frequently encountered polymers and their standardized abbreviations [58] [59].

Table 1: Common Polymer Acronyms and Chemical Names

Abbreviation	Chemical Name
ABS	Acrylonitrile Butadiene Styrene
ASA	Acrylonitrile Styrene Acrylate
EPDM	Ethylene Propylene Diene Monomer Rubber
EVOH	Ethylene Vinyl Alcohol
HDPE	High Density Polyethylene
LDPE	Low Density Polyethylene
PA	Polyamide (Nylon)
PC	Polycarbonate
PE	Polyethylene
PEEK	Polyetheretherketone
PET	Polyethylene Terephthalate
PMMA	Polymethylmethacrylate (Acrylic)
PP	Polypropylene
PS	Polystyrene
PTFE	Polytetrafluoroethylene
PU, PUR	Polyurethane
PVC	Polyvinyl Chloride
SAN	Styrene Acrylonitrile

Experimental Protocols for Terminology Resolution

Protocol: Automated Pre-Processing and Acronym Resolution in Textual Data

This protocol describes a methodology for extracting and resolving polymer acronyms from digital scientific documents, such as PDF files.

1. Reagent and Resource Solutions

Table 2: Key Research Reagents and Solutions for Data Extraction

Item	Function/Description
Polymer Acronym Reference List	A curated lookup table of known polymer abbreviations and their full names (e.g., as in Table 1 of this document). Essential for matching.
Natural Language Processing (NLP) Library (e.g., spaCy, SciSpacy)	Software tool for part-of-speech tagging, named entity recognition, and dependency parsing to identify chemical terms.
Regular Expression (Regex) Patterns	Logical text patterns to find acronyms, typically defined as uppercase words of 2-6 letters, often found in parentheses.
IUPAC Nomenclature Guidelines	Reference documents for structure-based naming rules to validate potential polymer names.

2. Procedure

• Text Extraction: Convert the target document (e.g., PDF, HTML) into raw, machine-readable text using an appropriate library (e.g., PyPDF2, pdfplumber for Python).
• Candidate Identification: Apply regular expression patterns (e.g., \([A-Z]{2,6}\)) to identify all parenthetical expressions that are potential acronyms. Simultaneously, use the NLP library to identify noun phrases that are potential full polymer names.
• Pair Matching: For each candidate acronym, scan the text immediately preceding the parentheses for a matching full name. The algorithm should check if the capital letters of the acronym correspond to the first letters of the words in the proposed full name.
• Lookup Table Validation: Cross-reference all identified acronyms and names against the curated Polymer Acronym Reference List. Flag any terms not found in the list for manual review.
• Contextual Validation: For validated polymer terms, use the NLP library to analyze the surrounding sentence structure to confirm the context is related to materials science (e.g., presence of words like "polymer", "blend", "composite", "mechanical properties").
• Data Output: Export the resolved polymer terms (both acronym and full name) into a structured format (e.g., CSV, JSON) for integration into a database or knowledge graph.

Protocol: Handling Historical Terminology and Trade Names

This protocol addresses the challenge of non-standard and historical names found in older literature and patents.

1. Reagent and Resource Solutions

Table 3: Key Reagents for Historical Terminology Resolution

Item	Function/Description
Historical Polymer Name Lexicon	A compiled dictionary mapping historical terms (e.g., "Bakelite", "Celluloid") and trade names (e.g., "Kevlar", "Teflon", "Nylon") to their standardized IUPAC or source-based names.
Document Metadata Analyzer	Tool to extract publication year, journal, and author information to assess the historical context of the document.

2. Procedure

• Metadata Analysis: Extract the publication date of the document. This establishes the historical context and likelihood of encountering non-standard terminology.
• Term Extraction: Identify unique, non-acronym chemical names using the NLP library. Focus on trademark symbols (, ) and capitalized product names.
• Lexicon Mapping: Query the Historical Polymer Name Lexicon with the extracted terms. For example, "Teflon" should map to "Polytetrafluoroethylene (PTFE)" and "Bakelite" should map to "Phenol-Formaldehyde resin".
• Manual Curation Interface: For unmapped terms, present them to a human expert via a simple interface with the source text context. The expert's decision to add the term to the lexicon should be recorded.
• Annotation and Storage: Store the original term from the text alongside its resolved standard name and the confidence level of the resolution (e.g., "automated lexicon match", "manually curated").

Workflow Visualization

The following diagram illustrates the logical workflow for resolving polymer terminology from a source document, integrating both automated and manual steps as described in the protocols.

For researchers in materials science and drug development, data governance is the foundational framework for defining and implementing policies, standards, and roles for data collection, storage, processing, and usage. Its primary aim is to ensure the quality, security, and availability of data throughout its entire lifecycle [60]. In the context of a research environment increasingly reliant on automated data extraction from scientific documents, data governance is intrinsically linked to data compliance—the practice of adhering to legal and regulatory requirements like the GDPR that govern how sensitive and personal data is handled and processed [60] [61].

The shift towards using large language models (LLMs) to extract structured data from unstructured materials science literature presents both tremendous opportunity and significant governance challenges [4] [17]. While these methods enable efficient extraction of data from vast sets of research papers, they introduce complexities in data visibility and control, especially when cloud solutions are involved. These environments often involve multiple providers, locations, and data formats, making it difficult to track data flows and usage [60]. Effective data governance requires a clear understanding of data sources, destinations, transformations, and dependencies, as well as clearly defined data ownership and access rights [60].

Governance Framework for Data Extraction Research

Core Components and Quantitative Requirements

A robust governance framework for data extraction initiatives must balance innovation with risk mitigation. The table below summarizes the core components and their associated quantitative targets based on established research.

Table 1: Core Components of a Data Governance Framework for Data Extraction Research

Governance Component	Function	Key Metric / Compliance Target
Data Quality Validation	Ensures accuracy and reliability of LLM-extracted data [4].	Precision and Recall rates close to 90% for extracted data triplets [4].
Regulatory Compliance	Adheres to data protection regulations (e.g., GDPR) [60] [61].	40% reduction in compliance violations via robust governance [61].
Access Control	Manages permissions for sensitive research data [60].	Role-based access enforced for all data classes.
Audit Trail	Tracks data access, extraction events, and modifications [60].	Immutable logging for all data transactions.
Data Stewardship	Assigns accountability for data management and integrity [61].	Clearly defined roles (e.g., Data Owner, Steward).

The Scientist's Toolkit: Research Reagent Solutions

The following tools and resources are essential for implementing the governance framework in a research setting.

Table 2: Essential Research Reagents & Solutions for Data Governance

Item	Function / Explanation
Conversational LLM (e.g., GPT-4)	Core engine for performing zero-shot data extraction from research texts with high accuracy [4].
Peer-Reviewed Protocol Databases (e.g., Springer Nature, Nature Protocols)	Provides validated, proven methodological procedures for experiments, serving as a benchmark for data quality [62].
Blockchain-Assisted Security Framework	Provides a decentralized and secure method for maintaining immutable audit trails and verifying data integrity [61].
CARE & FAIR Principles Checklist	Guidelines for Indigenous Data Governance and ensuring data is Findable, Accessible, Interoperable, and Reusable [61].
Automated Governance Tools (e.g., Fybrik)	Adds automation to manual governance and compliance processes, enabling secure data flow in cloud environments [60].

Experimental Protocols for Data Extraction and Validation

Protocol 1: Automated Data Extraction from Research Papers

This protocol details the methodology for using conversational LLMs, based on the ChatExtract method, to accurately extract materials data (e.g., Material, Value, Unit triplets) from unstructured text in research papers [4].

3.1.1 Initial Setup and Preparation

• Objective: To extract accurate materials property data from a corpus of scientific literature with minimal manual effort.
• Prerequisites:
  • Access to a conversational LLM API (e.g., GPT-4).
  • A collection of research papers (PDF or text format) relevant to the materials property of interest.
  • Python scripting environment for automation.

3.1.2 Step-by-Step Procedure

• Data Preparation: Gather target papers and remove any HTML/XML syntax. Use standard text parsing tools to divide the text into individual sentences [4].
• Relevance Classification (Stage A):
  • Apply a simple relevancy prompt to all sentences to identify those that contain the desired property data (value and units).
  • This step weeds out irrelevant sentences, typically reducing the dataset by about 99% [4].
  • Example Prompt: "Does the following sentence from a materials science paper contain a numerical value and a unit for [PROPERTY]? Sentence: '[SENTENCE]'. Answer only Yes or No."
• Context Expansion:
  • For each sentence classified as relevant, create a short passage consisting of:
    • The paper's title.
    • The sentence immediately preceding the target sentence.
    • The target sentence itself.
  • This helps capture the material's name, which may not be in the target sentence [4].
• Single vs. Multi-Valued Data Determination:
  • Use a prompt to determine if the passage contains a single data point or multiple data points.
  • Example Prompt: "Does the following text contain exactly one value for [PROPERTY]? Text: '[PASSAGE]'. Answer only Yes or No." [4]
• Data Extraction (Stage B):
  • For Single-Valued Texts: Ask separate, direct questions to extract the value, its unit, and the material's name. Explicitly allow for a negative answer to discourage hallucinations [4].
    • Example Prompts:
      • "What is the numerical value for the [PROPERTY] in the text? If not explicitly stated, answer 'Not Stated'."
      • "What is the unit for this value? If not explicitly stated, answer 'Not Stated'."
      • "What material does this value correspond to?"
  • For Multi-Valued Texts: Use a more rigorous series of uncertainty-inducing and redundant prompts to verify the correspondence between materials, values, and units. This involves asking follow-up questions that force the model to re-analyze the text [4].
    • Example Prompts:
      • "Extract all pairs of material and [PROPERTY] value from the text."
      • "For material [X], is the value [Y] with unit [Z]? Answer only Yes or No."

3.1.3 Validation and Quality Control

• Accuracy Check: Manually verify a statistically significant sample (e.g., 5-10%) of the extracted data against the original source text.
• Performance Metrics: Calculate precision and recall for the extraction process. The target, as demonstrated in research, is for both to be close to 90% [4].
• Data Correction: Implement a feedback loop where incorrectly extracted data is used to refine and improve the prompt engineering.

Protocol 2: Ensuring Data Security and Regulatory Compliance

This protocol outlines the steps for managing extracted data in a secure and compliant manner, aligning with governance frameworks [60] [63] [61].

3.2.1 Pre-Processing Setup

• Data Classification: Classify all extracted data according to its sensitivity (e.g., Public, Internal, Confidential, Regulated).
• Access Control Setup: Define and configure role-based access controls (RBAC) in your data storage system, ensuring the principle of least privilege.

3.2.2 Step-by-Step Procedure

• Secure Data Storage:
  • Encrypt all extracted data, both at rest and in transit.
  • Store data in a designated, secure environment with strict access logs. In cloud environments, this requires coordination to enforce data policies across the entire infrastructure [60].
• Data Anonymization/Pseudonymization:
  • If the extracted data set contains any personal data, apply appropriate de-identification techniques in accordance with GDPR or other relevant regulations [61].
• Audit Trail Implementation:
  • Activate and configure logging to record all access, modification, and extraction events related to the research data. Blockchain technology can be considered for creating immutable logs [61].
• Data Retention and Disposal:
  • Based on the data classification, apply defined retention policies.
  • Secfully and permanently delete data that has exceeded its retention period.

3.2.3 Compliance Verification

• Regular Audits: Conduct periodic internal audits of access logs and data handling procedures.
• Documentation: Maintain thorough documentation of all policies, procedures, and data breaches (if any), as required by regulations like GDPR [61].
• Stakeholder Training: Ensure all researchers and personnel involved are trained on data security and compliance protocols [63].

Workflow Visualization

The following diagram illustrates the integrated workflow for governing data extraction in a research context, from initial processing to secure storage.

Governance Workflow for Data Extraction

In the field of materials science informatics, a significant volume of critical historical data remains trapped within legacy systems and isolated data silos. These challenges severely hinder the application of modern data-driven research methods, such as machine learning and artificial intelligence, which require large-scale, structured, and accessible data [27]. The inability to fully utilize this data obstructs progress in materials discovery, optimization of preparation methods, and innovation in device applications [27]. This document provides detailed application notes and protocols for researchers and scientists engaged in the complex process of migrating from outdated data storage systems and integrating disparate data sources to construct a unified, accessible data ecosystem for advanced research.

Understanding the Challenges and Strategic Framework

The Problem: Legacy Systems and Data Silos in Research

Legacy systems—aging software or technologies critical to past operations—present substantial obstacles to growth and efficiency despite storing valuable data [64]. In a research context, these systems often rely on outdated technologies and non-standardized data formats, creating primary bottlenecks for researchers attempting to harness scientific data effectively [27] [65]. Concurrently, data silos—isolated pockets of information within individual departments or systems—prevent a unified view of data, leading to fragmented decision-making, operational inefficiencies, and stunted innovation [66]. For example, critical materials data stored in separate, incompatible systems (e.g., one system for thermal properties, another for synthesis details) prevents researchers from uncovering valuable correlations [67].

Foundational Principles for a Connected Data Ecosystem

To overcome these challenges, organizations should establish a connected data ecosystem built on these core principles [66]:

• Data Integration: Combining disparate data sources into a single, unified view using modern platforms.
• Data Governance: Establishing clear ownership, security, and compliance policies for consistent data usage.
• Data Quality Management: Ensuring data is clean, reliable, and actionable through ongoing audits and validation.
• Modern Tools and Technology: Equipping teams with technology that centralizes data management and fosters collaboration.

Application Notes: Migration and Integration Strategies

Legacy System Migration Strategies

Selecting the appropriate migration strategy is crucial and depends on factors such as the system's technical condition, business fit, and cost considerations [68]. The following table summarizes the most common strategies:

Table 1: Common Legacy System Migration Strategies

Strategy	Description	Best For
Rehosting (Lift-and-Shift) [64] [68]	Moving applications or systems to a new environment (e.g., cloud) without significant modifications.	Organizations seeking a quick, cost-effective migration with minimal changes [64].
Replatforming [64] [68]	Moving to a new platform with minor optimizations for the new environment.	Leveraging modern technologies while minimizing impact on the existing codebase [64].
Refactoring / Re-architecting [64] [68]	Redesigning and rebuilding the system from the ground up using modern architecture and principles.	Modernizing outdated, non-scalable architectures to fully leverage cloud-native technologies [64] [68].
Replacing with SaaS [68]	Retiring the old system and switching to a modern cloud product.	Commodity use cases like CRM where adjusting workflows to a new tool is feasible [68].
Phased Migration [64]	Dividing the migration process into distinct phases, moving components gradually.	Complex environments with intricate dependencies, to minimize disruption [64].
Strangler Pattern [68]	Gradually replacing specific legacy functions with modern services, often by wrapping them with APIs.	Modernizing complex systems in manageable stages without a full, risky cutover [68].

Strategies for Breaking Down Data Silos

Eliminating data silos requires a deliberate strategy that combines technology and organizational culture [66].

• Adopt a Unified Data Platform: Implement scalable platforms like Microsoft Fabric that integrate data engineering, warehousing, and analytics into a single environment [66]. Such platforms centralize data management, allowing every department to access and leverage the same data.
• Automate Data Integration: Use tools like Azure Synapse Analytics to create real-time pipelines that unify applications, databases, and data warehouses [66]. This reduces manual effort and ensures faster access to actionable insights.
• Foster a Data-Driven Culture: Technology alone is insufficient. Leadership must actively promote collaboration and shared goals across teams, encouraging open communication around data [66].
• Strengthen Governance Practices: Robust governance frameworks, enforced by platforms like Microsoft Purview, ensure that data remains secure, compliant, and trustworthy as it moves across the organization [66].

Experimental Protocols and Workflows

This section provides a detailed, actionable protocol for extracting and unifying materials data from legacy sources and siloed systems.

Protocol: Data Extraction from Legacy Literature and Systems

This protocol outlines a methodology for automating data extraction from unstructured scientific documents, a common legacy data source [24].

1. Objective: To automatically extract structured polymer-property data from a large corpus of full-text journal articles using a combination of heuristic filters, Named Entity Recognition (NER), and Large Language Models (LLMs) [24].

2. The Scientist's Toolkit:

Table 2: Key Research Reagent Solutions for Data Extraction

Item / Tool	Function
Corpus of Journal Articles	The primary source data, comprising full-text articles from publishers like Elsevier, Wiley, and ACS [24].
Heuristic Filters	Rule-based filters to detect paragraphs mentioning target properties or their co-referents, performing initial relevance screening [24].
Named Entity Recognition (NER) Model (e.g., MaterialsBERT)	A specialized model to identify and classify key entities like material names, properties, values, and units within the text [24].
Large Language Models (LLMs) (e.g., GPT-3.5, LlaMa 2)	Used to establish relationships between entities and extract information into a structured format, leveraging their advanced language understanding [24].
Polymer Scholar Website	A public platform to host and disseminate the extracted structured data for the wider scientific community [24].

3. Methodology:

• Step 1: Corpus Assembly and Identification: Assemble a corpus of materials science articles. Identify documents relevant to the target material class (e.g., polymers) by searching for specific keywords in titles and abstracts [24].
• Step 2: Text Unit Processing: Treat individual paragraphs within the identified documents as the primary text units for processing [24].
• Step 3: Two-Stage Filtering:
  • Heuristic Filter: Pass each paragraph through property-specific heuristic filters to detect mentions of target properties. This significantly reduces the volume of text for subsequent, more expensive processing [24].
  • NER Filter: Apply a NER model to the filtered paragraphs to confirm the presence of all necessary named entities (material, property, value, unit), ensuring the text contains a complete, extractable record [24].
• Step 4: Data Extraction and Structuring: Process the final set of relevant paragraphs through a data extraction pipeline. This can utilize a NER-based model like MaterialsBERT or an LLM like GPT-3.5 to identify the entities, establish relationships between them, and output the data in a structured format [24].
• Step 5: Data Validation and Dissemination: Conduct extensive evaluation of the extracted data's quality. Finally, make the validated dataset publicly available via a dedicated platform to accelerate community-wide research [24].

The following workflow diagram illustrates this multi-stage data extraction process:

Protocol: Phased Legacy System Migration

This protocol describes a phased approach to migrating a legacy system, which helps manage risk and ensures a controlled transition [64] [68].

1. Objective: To safely and effectively transition a legacy system to a modern environment through a series of planned phases, minimizing disruption to ongoing research activities.

2. Methodology:

• Phase 1: Assessment & Audit: Map the system's architecture, data structures, and external connections. Interview daily users to identify pain points, weaknesses, and requirements for the modernized system [68].
• Phase 2: Planning & Architecture: Create a detailed migration plan. This includes choosing a migration strategy (see Table 1), defining the target architecture, allocating resources, and identifying risks with fallback plans [68].
• Phase 3: Migration Execution: Execute the migration in controlled steps. This typically involves data migration (extracting, cleaning, and loading data) and application migration (rehosting, refactoring, or replacing the application), followed by integration and cutover to the new system [68].
• Phase 4: Testing & Validation: Rigorously test the new system. This includes functional testing, data validation, performance testing, and User Acceptance Testing (UAT) with real users to verify usability [68].
• Phase 5: Optimization & Ongoing Integration: Monitor the new system post-launch. Tune performance, train users, set up monitoring tools, and explore further integration opportunities. Finally, decommission the old legacy system [68].

The logical flow of this five-phase migration project is outlined below:

Discussion

Migrating from legacy systems and breaking down data silos are not merely technical tasks but strategic imperatives for accelerating data-driven research in materials science. The presented protocols provide a framework for overcoming these integration challenges. Success hinges on a methodical approach that includes careful assessment, selection of an appropriate migration strategy, and the implementation of a unified data platform supported by strong governance [66] [68]. By liberating data from outdated and isolated systems, research organizations can unlock the full potential of their historical data, thereby empowering advanced analytics, machine learning, and ultimately, fostering faster scientific discovery and innovation [27] [24].

Benchmarking for Success: Evaluating Model Performance and Data Accuracy

In the field of materials science, the acceleration of discovery cycles hinges on the ability to synthesize knowledge from vast scientific literature. An estimated 80% of experimental data remains locked in semi-structured formats within research papers, creating a significant bottleneck for knowledge-driven discovery [69]. Automated data extraction methods have emerged to overcome the limitations of manual curation, but their utility is entirely dependent on the quality and reliability of their outputs. This application note establishes a rigorous framework for evaluating extraction quality, focusing on the core metrics of precision and recall, and provides detailed protocols for their measurement within the context of materials science document research. These metrics are not merely academic; they form the foundation for building trustworthy, large-scale databases that can reveal novel composition-property relationships and guide the design of next-generation materials [69] [17].

Core Concepts: Quantifying Extraction Quality

The performance of any information extraction system is primarily quantified using precision and recall. These metrics provide a balanced view of a system's accuracy and completeness.

• Precision is the fraction of extracted data points that are correct. It measures the system's ability to avoid false positives or "hallucinations," where it reports data not present in the source text. It is calculated as: True Positives / (True Positives + False Positives).
• Recall is the fraction of all correct data points in the source text that were successfully extracted. It measures the system's ability to avoid false negatives, or missing relevant data. It is calculated as: True Positives / (True Positives + False Negatives).

The F1-score is the harmonic mean of precision and recall, providing a single metric to balance both concerns. A perfect system would achieve 100% precision and 100% recall, for an F1-score of 1.0 [69].

Table 1: Performance Benchmarks of Recent Data Extraction Frameworks in Materials Science

Framework / Model	Primary Extraction Target	Reported Precision (%)	Reported Recall (%)	Reported F1-Score (%)
ChatExtract (using GPT-4) [4]	Material, Value, Unit Triplets	90.8 - 91.6	83.6 - 87.7	~87
MatSKRAFT (Constraint-driven GNN) [69]	Property & Composition from Tables	90.35	87.07	88.68
Automated Training (MatSKRAFT w/ Annotation Algorithms) [69]	Various Material Properties	89.12	88.64	88.88

Experimental Protocols for Metric Evaluation

This section provides a detailed, step-by-step protocol for establishing the ground truth and calculating the performance metrics for a data extraction system.

Protocol: Creation of a Manually Annotated Gold Standard Test Set

Objective: To create a reliable benchmark dataset for evaluating the precision and recall of a data extraction pipeline. Reagents and Solutions:

• Source Corpus: A representative sample of materials science literature (e.g., PDFs or XML files).
• Annotation Software: A tool for marking text spans (e.g., BRAT, Prodigy, or a custom in-house system).
• Expert Annotators: Domain experts (e.g., materials scientists) capable of identifying target data.

Methodology:

• Dataset Sizing: Select a statistically significant number of documents or text passages. For example, the MatSKRAFT framework used a test set of 737 tables for property extraction [69].
• Annotation Guideline Development: Create a detailed document defining the target data (e.g., "critical cooling rate," "yield strength"), the format for extraction (e.g., Material, Value, Unit triplet), and rules for handling ambiguous cases.
• Blind Annotation: Have at least two domain experts annotate the same set of documents independently. This enables the measurement of inter-annotator agreement, which validates the consistency of the ground truth.
• Adjudication: Resolve discrepancies between annotators through discussion or via a third senior expert to produce a single, consolidated ground truth dataset.
• Curation: Store the final annotations in a structured format (e.g., JSON) for easy comparison with system outputs.

Protocol: Calculation of Precision, Recall, and F1-Score

Objective: To quantitatively measure the performance of an extraction system against the gold standard test set. Reagents and Solutions:

• Gold Standard Test Set: The dataset created in Protocol 3.1.
• System Outputs: The extractions generated by the system for the same documents in the test set.
• Evaluation Script: A script (e.g., in Python) to compare system outputs against the ground truth.

Methodology:

• Alignment: Map each data point extracted by the system to its corresponding data point in the gold standard. A match is typically counted only if all relevant fields (e.g., material, property, value, unit) are correct.
• Count Classification:
  • True Positive (TP): A data point that is present in the gold standard and was correctly extracted by the system.
  • False Positive (FP): A data point extracted by the system that is not present in the gold standard (incorrect extraction or hallucination).
  • False Negative (FN): A data point present in the gold standard that the system failed to extract.
• Metric Calculation:
  • Calculate Precision: ( P = TP / (TP + FP) )
  • Calculate Recall: ( R = TP / (TP + FN) )
  • Calculate F1-score: ( F1 = 2 * (P * R) / (P + R) )
• Error Analysis: Manually review FP and FN cases to identify common failure modes (e.g., difficulty with multi-valued sentences, specific unit formats, or complex table structures) and guide future system improvements [4].

Workflow Visualization: Data Extraction Quality Assessment

The following diagram illustrates the end-to-end process for developing and evaluating a data extraction system, from initial setup to final performance assessment.

The Scientist's Toolkit: Research Reagents and Computational Frameworks

Building and evaluating a high-quality data extraction system requires a combination of data, tools, and computational frameworks.

Table 2: Essential Components for Materials Data Extraction Research

Item / Framework	Type	Function in Research
Gold Standard Test Set	Data	Serves as the ground truth benchmark for quantitatively evaluating precision and recall [69].
Conversational LLM (e.g., GPT-4)	Tool	Powers zero-shot extraction methods like ChatExtract, which uses prompt engineering to identify and verify data [4].
Constraint-Driven Graph Neural Network (GNN)	Framework	Specialized architecture, as in MatSKRAFT, that encodes scientific principles to accurately parse complex table structures [69].
Distant Supervision Data	Data & Method	Technique using existing databases (e.g., INTERGLAD) to automatically generate training labels, overcoming data scarcity [69].
Annotation Algorithms	Software	Domain-informed rules that programmatically label data, expanding training coverage and improving model precision [69].
Viz Palette Tool	Tool	Utility for testing color accessibility in resulting data visualizations, ensuring findings are accessible to all [70].
Color Contrast Analyzer	Tool	Tool to verify that workflow diagrams and user interfaces meet WCAG guidelines for legibility [71] [72].

The acceleration of materials discovery is heavily dependent on the ability to extract and structure vast amounts of scientific knowledge trapped in published literature. Within this context, Natural Language Processing (NLP) and Large Language Models (LLMs) have emerged as powerful tools for automated data extraction. This application note provides a performance and cost analysis of three distinct models—GPT-3.5, LlaMa 2, and MaterialsBERT—for the specific task of data extraction from materials science documents. The insights herein are designed to guide researchers, scientists, and drug development professionals in selecting and implementing the most efficient model for their informatics pipelines, framed within a broader thesis on optimizing data extraction workflows.

The selected models represent different approaches to NLP: two are versatile, general-purpose LLMs, and one is a specialized, domain-trained model.

• GPT-3.5 (Generative Pre-trained Transformer 3.5) is a proprietary, closed-source model developed by OpenAI. It is a decoder-only transformer, fine-tuned with Reinforcement Learning from Human Feedback (RLHF) for conversational tasks. It is accessible via an API and is known for its strong general-purpose capabilities [73].
• LlaMa 2 (Large Language Model Meta AI 2) is an open-source model suite released by Meta. It also uses an optimized transformer architecture and was fine-tuned for dialogue using RLHF. Its open-source nature allows for extensive customization and on-premise deployment, which is critical for data-sensitive applications [74] [73].
• MaterialsBERT is a domain-specific language model for materials science. It is based on the BERT (Bidirectional Encoder Representations from Transformers) architecture and was created by further pre-training the SciBERT model on a large corpus of peer-reviewed materials science publications. This process allows it to develop a superior understanding of domain-specific notations and jargon [22] [40].

Table 1: Core Model Characteristics and Capabilities

Characteristic	GPT-3.5	LlaMa 2 (70B)	MaterialsBERT
Developer	OpenAI	Meta	Materials Science Research Community
Source Availability	Proprietary	Open-Source	Open-Source
Primary Architecture	Decoder-only Transformer	Transformer	Encoder-only Transformer (BERT)
Key Strength	Ease of use, strong out-of-the-box performance	Customizability, data privacy, cost-effectiveness for self-hosting	Domain-specific knowledge, high precision on scientific NER
Context Window	16,000 tokens [75]	4,000 tokens [74]	512 tokens (standard BERT limit)
Knowledge Cutoff	September 2021 [75]	Information not available in search results	Trained on historical scientific literature
Multimodal Capabilities	Text-only (GPT-3.5) [75]	Text-only [74]	Text-only

Quantitative Performance and Cost Analysis

A critical step in model selection is evaluating performance against cost. The following data synthesizes benchmarks from general NLP tasks and specific materials science applications.

Table 2: Performance and Cost Benchmarking

Metric	GPT-3.5	LlaMa 2 (70B)	MaterialsBERT
General Benchmark Performance	Lower than GPT-4 on reasoning & exams; higher hallucination rates [75]	Competitive with GPT-3.5, can outperform it on some benchmarks [73] [76]	State-of-the-art on materials science NER tasks [22]
Materials Science QA (MaScQA) Accuracy	Lower than GPT-4 [77]	Lower than GPT-4 [77]	Not Applicable (Not a generative QA model)
Data Extraction Quality	Used successfully in polymer data extraction [24]	Used successfully in polymer data extraction [24]	High-quality polymer-property record extraction [24] [40]
Inference Cost	~$0.50 per 1M tokens (output) [78]	Significant cost savings for self-hosted deployment [79] [76]	Highest cost-efficiency for its specialized NER task [24]
Inference Speed	Fast (API-based)	Varies based on hosting infrastructure	Optimized for fast batch processing of texts [40]
Hallucination Rate	Higher (e.g., ~40% fabrication rate in citations) [75]	Generally lower than GPT-3.5 due to enhanced safety training [74]	Low for its designed NER tasks (deterministic extraction)

Experimental Protocols for Data Extraction

This section outlines a standardized workflow and protocol for using these models to extract polymer-property data from scientific literature, based on a published framework [24].

Workflow for Large-Scale Polymer Data Extraction

The following diagram illustrates the end-to-end pipeline for processing a large corpus of journal articles to extract structured polymer-property data.

Diagram 1: High-level workflow for extracting polymer-property data from a large corpus of scientific articles, adapted from [24]. The pipeline involves identifying relevant documents, filtering paragraphs likely to contain data, and finally extracting structured information.

Protocol 1: Two-Stage Paragraph Filtering

Objective: To efficiently identify text paragraphs that contain extractable polymer-property data, thereby reducing unnecessary and costly processing by LLMs.

• Heuristic Filtering:

  • Input: All paragraphs from a polymer-related article.
  • Action: Pass each paragraph through a set of property-specific heuristic filters. These filters consist of manually curated lists of keywords and co-referents for the 24 target properties (e.g., "glass transition temperature," "T_g," "refractive index").
  • Output: Approximately 11% of paragraphs (~2.6 million from a starting set of ~23.3 million) pass this initial filter [24].

• NER Filtering:

  • Input: Paragraphs that passed the heuristic filter.
  • Action: Process the paragraphs with a Named Entity Recognition (NER) model (like MaterialsBERT) to verify the presence of all necessary entities for a complete data record: material, property, value, and unit.
  • Output: Approximately 3% of the original paragraphs (~716,000) pass this second filter, confirming they contain a likely extractable property record [24].

Protocol 2: Structured Data Extraction with LLMs and NER

Objective: To convert the filtered, relevant paragraphs into a structured data format (e.g., JSON) containing the material name, property, value, and unit.

• Model Setup:

  • For GPT-3.5, use the OpenAI Chat Completions API with a carefully designed prompt for information extraction.
  • For LlaMa 2, deploy the model locally or on a private cloud instance. Use the same prompt structure as for GPT-3.5 to ensure a fair comparison.
  • For MaterialsBERT, utilize a trained NER model within a pipeline that combines identified entities into records using heuristic rules or a relation classification model.

• Prompting for LLMs (GPT-3.5 & LlaMa 2):

  • Employ a few-shot learning approach. The prompt should include:
    • System Message: Define the role of the AI as an expert in materials science data extraction.
    • Task Instructions: Clearly state the requirement to extract the polymer name, property name, numerical value, and unit from the given text. Specify the output format (e.g., JSON).
    • Few-Shot Examples: Provide 2-3 examples of input text and the corresponding, correctly formatted output JSON.
    • Target Text: The paragraph to be processed.

• Extraction with MaterialsBERT:

  • The model is not prompted but is used to tag tokens in the input text with entity labels (e.g., B-POLYMER, I-PROPERTY_VALUE).
  • A downstream processing script aggregates these tagged tokens to form complete entity spans.
  • A rule-based or machine learning-based relation classifier is then used to associate the correct value and unit with a material-property pair.

The Scientist's Toolkit: Research Reagent Solutions

This section details the essential "reagents"—the models, data, and software—required to replicate the data extraction protocols.

Table 3: Essential Resources for Data Extraction Experiments

Resource Name	Type	Function / Application	Access / Source
GPT-3.5 Turbo API	Proprietary LLM	Used for the final data extraction step via API calls with few-shot prompts. Optimizes for ease of use and rapid prototyping.	OpenAI API
LlaMa 2 (70B Chat)	Open-Source LLM	Used for the final data extraction step in self-hosted environments. Optimizes for data privacy, customization, and long-term cost-efficiency.	Hugging Face, Meta
MaterialsBERT Model	Domain-Specific NER Model	Used for the NER filtering step and as a standalone data extractor. Optimizes for accuracy and cost on entity recognition tasks specific to polymers.	Hugging Face ( [40])
Polymer Scholar Corpus	Dataset	A large corpus of ~2.4 million materials science articles, from which ~681,000 polymer-related documents can be identified. Serves as the input data for the workflow.	Polymer Scholar website [24]
Annotation Tool (Prodigy)	Software	A scriptable, active learning-based annotation tool used for creating labeled datasets for training and evaluating NER models.	Prodi.gy

Analysis of Results and Recommendations

The performance of GPT-3.5, LlaMa 2, and MaterialsBERT must be evaluated across several dimensions relevant to a research setting: quality, cost, speed, and applicability.

• Performance on Materials Science Tasks: In a benchmark study (MaScQA) designed to test materials science knowledge, GPT-4 significantly outperformed both GPT-3.5 and LlaMa 2, with GPT-3.5 showing lower accuracy [77]. However, for the specific task of information extraction from polymer literature, all three models have been successfully employed to create a database of over one million property records [24]. MaterialsBERT, being domain-adapted, establishes state-of-the-art performance on NER tasks within materials science [22] [40].

• Cost and Efficiency Considerations: The cost dynamics are complex. GPT-3.5 incurs a predictable, per-token API cost, which can become substantial at scale but requires no infrastructure management [75]. LlaMa 2, being open-source, eliminates API fees for self-hosted deployments, leading to reported savings of 40-60% compared to proprietary models [79]. The most significant cost optimization, as demonstrated in the workflow (Diagram 1), comes from the two-stage filtering that minimizes the number of expensive LLM calls. Using MaterialsBERT for filtering is highly cost-effective, as it is optimized for fast, accurate NER on scientific text [24].

• Recommendations for Implementation:

  • For Maximum Accuracy with Domain Specificity: Use a hybrid pipeline. Employ MaterialsBERT for the initial NER filtering and for applications where the highest precision on entity recognition is required. Reserve general-purpose LLMs like GPT-3.5 or LlaMa 2 for complex relationship extraction or tasks requiring broader synthesis.
  • For Data-Sensitive or Customized Applications: Choose LlaMa 2. Its open-source license allows for fine-tuning on proprietary datasets and deployment in secure, on-premise environments, ensuring full data control [74] [73].
  • For Rapid Prototyping and Ease of Use: GPT-3.5 offers a fast path to implementation via a well-documented API, making it suitable for initial exploration and projects where data privacy is not the primary concern [73].

In conclusion, the choice between GPT-3.5, LlaMa 2, and MaterialsBERT is not a matter of selecting a single "best" model, but rather of strategically deploying each according to its strengths within a data extraction pipeline. Combining the high-efficiency, domain-specific filtering of MaterialsBERT with the powerful generative capabilities of LLMs presents a robust and cost-effective strategy for unlocking the wealth of information contained in materials science literature.

In the data-centric era of materials science, the expansive production and sharing of research data necessitates robust stewardship to ensure that extracted data is not only available but also trustworthy and fit for repurposing [80]. Validation frameworks provide the documented evidence that data extraction processes consistently yield results meeting predetermined specifications for quality, integrity, and reliability [81]. Within materials science, where data forms the basis for identifying critical process-structure-property relationships, a rigorous auditing plan is non-negotiable for both computational and experimental data [82].

Adherence to the FAIR data principles (Findable, Accessible, Interoperable, and Reusable) provides a foundational ethos for validation frameworks, ensuring data is richly annotated and reusable beyond its original purpose [80]. For researchers extracting data from materials science documents, a validation framework minimizes operational risk, upholds regulatory compliance, and ensures that data-driven conclusions about material behavior are built upon a reliable foundation [81].

Core Principles of a Data Validation Framework

A robust validation plan for auditing extracted data is built upon three interdependent core principles, which together ensure comprehensive data integrity throughout its lifecycle.

Foundational Data Quality Dimensions

Data validation must assess multiple dimensions of data quality to ensure fitness for use. These dimensions provide the quantitative and qualitative metrics for auditing data extraction outputs [83].

• Validity: Data must conform to defined formats, values, and business rules specific to materials science (e.g., ensuring crystal structure notation follows standardized formats, chemical formulas are syntactically correct).
• Completeness: All required data fields extracted from source documents must be populated. Missing data for critical attributes, such as a material's yield strength or a processing temperature, can render a dataset useless for establishing structure-property relationships [83].
• Consistency: Extracted data must be reliable and in a consistent format across all datasets and source documents. Inconsistent units of measurement (e.g., MPa vs. psi) or terminology for material states (e.g., "solution treated" vs. "solution annealed") introduce significant errors in analysis [83].
• Accuracy: The extracted data must correctly represent the values and facts as presented in the original source material. This is a measure of correctness, ensuring that data entry or extraction errors do not alter the fundamental meaning of the data [83].
• Uniqueness: Each data entity should be represented only once to prevent duplication that could skew analytical models and lead to incorrect conclusions about material behavior [83].

The Validation Lifecycle: IQ, OQ, PQ

The qualification of any system or process, including those for data extraction, follows a proven three-stage validation lifecycle. This structured approach, adapted from analytical instrument qualification, provides a framework for validating the tools and methods used in data extraction [81].

• Installation Qualification (IQ): Confirms that the data extraction tool or software platform is correctly installed and configured according to specifications. This includes verifying all necessary libraries, dependencies, and connections to data sources are operational.
• Operational Qualification (OQ): Demonstrates that the extraction tool operates as intended throughout its anticipated operating ranges. This involves testing its core functions, such as parsing different file formats (PDF, HTML, DOC), correctly identifying and extracting data from tables and text, and handling anomalous document structures.
• Performance Qualification (PQ): Provides documented evidence that the data extraction process consistently performs according to predefined specifications in its routine operational environment. This is demonstrated through long-term testing with real-world materials science documents, confirming that the output data meets all quality attributes for accuracy, completeness, and consistency over time [81].

Risk-Based Validation Approach

A modern validation strategy employs a risk-based approach, directing resources and validation rigor towards the most critical data elements. The risk assessment process for data extraction involves [81]:

• Identifying Critical Data Attributes: Determining which extracted data elements (e.g., material composition, processing parameters, mechanical properties) are most critical to the final research outcomes or product quality.
• Assessing Extraction Process Risk: Evaluating the complexity and potential failure modes of the extraction process for each critical data attribute. For instance, extracting numerical values from well-structured tables carries lower risk than extracting semantic meaning from free-text experimental descriptions.
• Implementing Control Strategies: Establishing mitigation mechanisms for high-risk extraction processes, such as enhanced review steps, automated cross-verification with other data sources, or tighter acceptance criteria for data quality checks.

Quantitative Data Quality Metrics and Assurance

A validation framework requires quantitative metrics to objectively assess and assure data quality. The following protocols and checks form the basis of a rigorous data auditing plan.

Data Quality Assessment Table

The table below outlines key data quality dimensions, their definitions, and corresponding metrics for auditing extracted data.

Table 1: Data Quality Dimensions and Metrics for Auditing Extracted Data

Quality Dimension	Definition	Quantifiable Metric(s)	Target Threshold
Completeness [83]	The degree to which all required data is present.	Percentage of missing values per required field.	< 5% missing for critical fields [84].
Accuracy [83]	The correctness of the data against the source.	Error rate (number of incorrect values / total values checked).	< 1% error rate.
Consistency [83]	The absence of contradiction between related data items.	Number of logical conflicts (e.g., a final heat treatment temperature recorded before the initial melting step).	Zero logical conflicts.
Uniqueness [83]	The non-duplication of data records.	Number of exact duplicate records.	Zero exact duplicates.
Validity [83]	Conformance to a defined format or syntax.	Percentage of values conforming to syntax rules (e.g., date format, numeric range).	> 99.5% conformance.
Timeliness [83]	The availability of data within an expected timeframe.	Time elapsed from source data publication to extraction and availability.	As defined by project requirements.

Data Cleaning and Assurance Protocol

Prior to analysis, extracted data must undergo a rigorous cleaning process. The following step-by-step protocol is essential for quality assurance [84].

• De-duplication: Identify and remove identical copies of data, leaving only unique records. This is critical when aggregating data from multiple sources to prevent skewed analysis.
• Missing Data Analysis: Calculate the percentage of missing data per field and per record. Use a statistical test, such as Little's Missing Completely at Random (MCAR) test, to analyze the pattern of missingness. Establish thresholds for record inclusion/exclusion (e.g., retain only records with >80% data completeness for critical fields) [84].
• Anomaly Detection: Run descriptive statistics (e.g., min, max, mean, standard deviation) for all numerical measures to identify outliers and values that deviate from expected patterns. Visually inspect distributions for unexpected skewness or kurtosis. Cross-check anomalies against the original source document.
• Data Transformation and Summation: Where applicable, transform data according to predefined rules. This may include unit conversions, or summation of individual questionnaire or data sheet items into broader constructs or scores as per the original instrument's guidelines [84].
• Verification of Psychometric Properties: For any standardized instrument or data collection methodology embedded in the literature, report or verify known psychometric properties, such as reliability (e.g., Cronbach's alpha > 0.7) to ensure the construct validity of the extracted data [84].

Experimental Protocol for Validating Extracted Data

This detailed protocol provides a reproducible methodology for auditing and validating data extracted from materials science documents.

Scope and Application

This protocol applies to the audit of data extracted from scientific literature, technical reports, and internal research documents within materials science and engineering. It is designed to be applied post-extraction to a defined dataset.

Materials and Reagents

Table 2: Research Reagent Solutions for Data Validation

Item Name	Function / Description	Example / Specification
Reference Dataset	A pre-validated "gold standard" dataset used to benchmark the accuracy and performance of the data extraction process.	A manually curated dataset from 50 materials science papers with verified data points.
Syntax Rule Set	A collection of machine-readable rules that define valid formats, value ranges, and allowed terms for specific data fields.	Regular expressions for chemical formulas (e.g., Ni_{x}Al_{y}, where x+y=100), temperature ranges (e.g., 0 - 2000 °C).
Ontology/Taxonomy	A controlled vocabulary that standardizes terminology for materials, processes, and properties, ensuring semantic consistency.	Materials science ontologies covering terms like "creep resistance," "austenitization," or "CMSX-6 superalloy" [80] [82].
Statistical Analysis Software	Software used to perform statistical quality checks, including descriptive statistics, normality tests, and missing data analysis.	R, Python (with Pandas, NumPy), or SPSS.
Contrast-Finder Tool	A web-based tool to ensure sufficient color contrast for any data visualizations or dashboards generated from the extracted data, complying with WCAG guidelines [85].	WebAIM's Contrast Checker or App.Contrast-Finder.org.

Step-by-Step Procedure

Pre-Validation Setup

• Define the Audit Scope: Identify the specific dataset to be audited, including the source documents and the data fields extracted.
• Establish Acceptance Criteria: Define the pass/fail thresholds for each data quality metric from Table 1 (e.g., completeness >95%, accuracy >99%).
• Prepare the Validation Protocol: Document the sampling plan (e.g., audit 20% of randomly selected records, or 100% of critical fields) and the exact tests to be performed.

Execution of Data Quality Checks

• Run Automated Checks: Execute scripts to assess validity, uniqueness, and completeness against the predefined rule sets and the entire dataset.
• Perform Manual Spot-Check: For accuracy assessment, randomly select a subset of extracted data points (e.g., 5-10% of records) and compare them directly to the original source document. Calculate the error rate.
• Conduct Logical Consistency Review: Use query tools to identify logical inconsistencies (e.g., a material reported with a tensile strength greater than its theoretical maximum, or a processing timeline that is impossible).

Data Analysis and Reporting

• Compile Results: Aggregate the results from all checks into a validation report.
• Compare to Acceptance Criteria: For each quality dimension, state whether the result passed or failed the predefined acceptance criterion.
• Document Deviations: Record any deviations from the protocol and any anomalies encountered during the audit. The final report must present a clear summary of the dataset's quality and its fitness for the intended use [84].

Workflow Visualization of the Validation Framework

The following diagram illustrates the end-to-end logical workflow for auditing extracted data, incorporating the core principles, protocols, and risk assessment.

Figure 1. End-to-end workflow for validating extracted data.

A robust plan for auditing extracted data is a critical component of modern materials science research. By integrating foundational data quality principles, a structured validation lifecycle, and a risk-based approach, researchers can construct a defensible framework that ensures the reliability of their data [81]. The provided protocols, metrics, and workflows offer a concrete path to achieving FAIR data compliance, thereby enhancing the reproducibility and reusability of research outputs [80] [82]. In an era driven by data-centric discovery, such validation frameworks are not merely administrative exercises but are fundamental to building trustworthy process-structure-property relationships and accelerating materials innovation.

In the field of data extraction from materials science documents, the integration of human intelligence with automated systems is crucial for managing complex, unstructured data. Human-in-the-Loop (HITL) machine learning represents a paradigm where human interaction, intervention, and judgment directly control or change the outcome of a process [86]. This approach is particularly valuable in materials science, where data heterogeneity, specialized terminology, and the need for domain expertise present significant challenges to fully automated extraction systems. HITL frameworks ensure that the final data output maintains the high degree of accuracy required for downstream research and development activities, including drug development and materials optimization.

Depending on who is in control of the learning process, different HITL approaches can be identified: Active Learning (AL), where the system remains in control and uses humans as oracles to annotate data; Interactive Machine Learning (IML), characterized by closer, more frequent interaction; and Machine Teaching (MT), where human domain experts have control over the learning process [87]. Understanding these distinctions helps in designing appropriate verification workflows for materials science data extraction.

When to Incorporate Manual Verification

Manual verification becomes essential in several key scenarios within the materials science data lifecycle. The quantitative decision criteria for incorporating manual verification are summarized in the table below.

Table 1: Decision Criteria for Manual Verification in Data Extraction

Criterion	Quantitative Threshold	Verification Protocol
Low Confidence Predictions	ML confidence score < 90%	Active Learning with expert annotation [87]
Complex Data Relationships	Entity relations > 3 connections	CREDAL methodology for close reading of data models [88]
Novel or Unseen Terminology	Term frequency < 5 in corpus	Machine Teaching with domain expert input [87]
Contradictory Source Information	Conflicting values ≥ 2 sources	ONION participatory modeling framework [88]
Critical Pathway Data	Drug development milestones	Multi-stage verification protocol

High-Stakes Extraction Scenarios

In materials science research with direct implications for drug development, manual verification is non-negotiable for certain data types. This includes extracted material properties used in pharmaceutical formulations, synthesis protocols with safety implications, and experimental results that directly influence research directions. For example, in autonomous materials exploration campaigns for composition-structure phase mapping, human input through indicated phase boundaries or regions of interest significantly improves phase-mapping performance [89]. Similarly, when determining table unionability for data discovery, a combination of human and machine intelligence outperforms either approach alone [88].

Data Quality Indicators Requiring Verification

Specific data quality flags should automatically trigger manual verification protocols. These include low confidence scores from machine learning classifiers (<90% confidence), inconsistent units of measurement across extractions, missing critical data fields in experimental protocols, and ambiguous semantic relationships between entities. The CREDAL methodology, which involves close reading of data models as artifacts, provides a systematic approach for identifying and resolving such ambiguities [88].

How to Implement Manual Verification: Protocols and Workflows

Effective implementation of manual verification requires structured protocols and clear workflows. The following experimental protocols provide detailed methodologies for key verification scenarios.

Protocol 1: Active Learning for Annotation Verification

Purpose: To efficiently validate machine-generated annotations of materials science entities using human expertise in a system-controlled framework.

Materials:

• Unlabeled or machine-annotated materials science documents
• Domain expert annotators (materials scientists, chemists)
• Annotation interface with query strategy implementation

Procedure:

• Initial Model Training: Train initial named entity recognition (NER) model on seed set of 200-500 human-annotated documents.
• Uncertainty Sampling: Deploy model to classify new documents, flagging instances with prediction confidence between 60-90% for verification.
• Expert Verification: Present flagged extractions to domain experts through specialized interface showing:
  • Original document context
  • Machine-predicted annotation
  • Confidence score
  • Alternative predictions (if available)
• Correction and Validation: Experts correct mislabeled entities and confirm correct predictions.
• Model Retraining: Incorporate verified annotations into training set and retrain model.
• Iteration: Repeat steps 2-5 until target accuracy (typically >95%) is achieved.

Quality Control: Implement inter-annotator agreement measures with Cohen's Kappa >0.8 for multiple experts.

Protocol 2: Interactive Refinement of Extraction Rules

Purpose: To leverage human domain knowledge in refining and improving automated extraction rules through close collaboration between domain experts and data scientists.

Materials:

• Set of documents with known extraction errors
• Rule-authoring environment
• Domain experts and data scientists

Procedure:

• Error Analysis Session: Conduct collaborative session to review false positives/negatives from current extraction system.
• Pattern Identification: Domain experts identify linguistic and contextual patterns missed by current system.
• Rule Formulation: Data scientists translate expert-identified patterns into formal extraction rules.
• Immediate Testing: Test new rules on sample documents with expert feedback.
• Iterative Refinement: Refine rules based on immediate feedback in 2-3 rapid cycles.
• Validation: Deploy refined rules to full corpus with precision/recall monitoring.

This interactive approach aligns with Interactive Machine Learning principles, where closer interaction between users and learning systems enables more focused, frequent, and incremental improvements compared to traditional machine learning [87].

Protocol 3: Multi-Stage Verification for Critical Data

Purpose: To ensure maximum accuracy for mission-critical extractions (e.g., materials properties for drug development) through layered verification.

Materials:

• Extracted data flagged as critical
• Multiple domain experts with complementary expertise
• Version-controlled database

Procedure:

• Primary Extraction: Automated system performs initial data extraction.
• First-Pass Verification: Junior researcher verifies extractions against source documents, flagging ambiguities.
• Expert Review: Senior scientist reviews flagged extractions and sample of verified data (10-20%).
• Consensus Resolution: Panel review for contentious or high-impact extractions.
• Final Validation: Cross-check with external sources or experimental validation when possible.

Visualization of Verification Workflows

Figure 1: High-Level Verification Workflow for Data Extraction

Figure 2: Detailed Verification Process with Dual Pathways

The Scientist's Toolkit: Research Reagent Solutions

Table 2: Essential Tools for Human-in-the-Loop Data Extraction

Tool Category	Specific Solution	Function in Verification Workflow
Annotation Platforms	BRAT, Prodigy, INCEpTION	Provide interfaces for human annotators to verify and correct machine extractions [88]
Active Learning Systems	modAL, ALiDa	Implement query strategies to identify most valuable examples for human verification [87]
Collaborative Frameworks	ONION participatory framework	Support multiple stakeholder input in model development [88]
Version Control Systems	Git, DVC	Track changes to extraction rules and verified datasets
Quality Metrics	Precision, Recall, F1-score, IoU	Quantify extraction performance and verification effectiveness
Explainability Tools	LIME, SHAP, Anchors	Provide explanations for model predictions to guide human verification [87]

Implementation Guidelines and Best Practices

Designing Effective Human-AI Collaboration

Successful implementation of HITL systems requires careful attention to the human experience around AI models. This includes focusing on both "Usable AI" (ensuring AI systems are usable by the people interacting with them) and "Useful AI" (making AI models useful to the society in which they are embedded) [87]. In practice, this means:

• Intuitive Interfaces: Verification interfaces should present context efficiently, minimizing cognitive load on expert verifiers.
• Appropriate Automation: Identify which verification tasks are best performed by humans versus machines, leveraging human judgment for complex semantic decisions.
• Feedback Integration: Create closed-loop systems where human corrections directly improve automated extraction systems.

Managing Verification Teams and Workflows

The effectiveness of HITL systems depends significantly on human factors and team management. Research shows that data science managers play a critical role in navigating the advantages and challenges of distributed data science teams [86]. Key considerations include:

• Role Definition: Clearly define responsibilities between domain experts, data scientists, and verification specialists.
• Training: Provide adequate training on verification tools and guidelines to ensure consistency.
• Quality Assurance: Implement regular inter-annotator agreement checks and ongoing calibration sessions.

As demonstrated in materials science applications, appropriate human input through indicated phase boundaries or regions of interest significantly improves analytical performance [89]. This principle extends to data extraction, where strategic human verification targets areas of greatest uncertainty or importance.

Application Note

The expansion of materials science is characterized by a rapidly growing body of scientific literature. However, a significant portion of critical experimental data—encompassing composition, processing parameters, microstructure, and properties—remains trapped in unstructured text, creating a bottleneck for data-driven discovery [27] [28]. Automated data extraction technologies have emerged to overcome this limitation, but their ultimate value is determined by the reliability and downstream utility of the data they produce. This application note assesses the real-world impact of extracted data by evaluating the performance of advanced extraction methodologies, focusing on their applicability in downstream research tasks such as machine learning and materials informatics.

Performance Benchmarking of Extraction Methodologies

The reliability of data extraction pipelines is quantitatively benchmarked using standard performance metrics, including precision, recall, and F1 score. The following table summarizes the performance of state-of-the-art methods as reported in recent literature.

Table 1: Performance Metrics of Advanced Data Extraction Pipelines

Extraction Method	Core Innovation	Reported Precision (%)	Reported Recall (%)	Reported F1 Score	Key Application Context
Multi-Stage LLM with Source Tracking [28]	Iterative extraction with source tracking and validation stages.	~96 (Feature-level)	~96 (Feature-level)	0.959 (Feature-level)	Extracting 47 features across composition, processing, microstructure, and property relationships from full-text articles.
ChatExtract [4]	Conversational LLM with engineered prompts and follow-up questions to reduce hallucinations.	91.6	83.6	~0.88* (Calculated)	Building databases for critical cooling rates of metallic glasses and yield strengths of high-entropy alloys.
AI-Human Hybrid (Claude 3.5) [90]	AI-assisted single extraction followed by human verification.	Study Ongoing (Results expected 2026)	Study Ongoing (Results expected 2026)	N/A	Extracting event counts and group sizes from randomized controlled trials (RCTs) in systematic reviews.

Note: The F1 score for ChatExtract is an approximation based on the provided precision and recall values. The study in [90] is a randomized controlled trial in progress, and its results will provide a direct comparison between AI-human hybrid and traditional human double-extraction methods.

Impact on Downstream Research

The reliability of these automated methods has a direct and measurable impact on downstream research:

• Database Construction: High-precision extraction (F1 scores >0.9) enables the creation of large-scale, structured databases from historical literature with minimal false positives, providing a trustworthy foundation for materials informatics [28].
• Research Efficiency: Automated frameworks significantly accelerate the data collection process. For instance, the multi-stage LLM pipeline successfully processed 100 journal articles on multi-principal element alloys, identifying and extracting detailed information for 396 materials [28]. This efficiency allows researchers to shift focus from manual data curation to analysis and discovery.
• Comprehensive Data Capture: Beyond simple property extraction, modern pipelines capture the complex, hierarchical composition–processing–microstructure–property relationships that are essential for understanding materials behavior [28]. This holistic data capture is critical for developing predictive models that accurately reflect real-world materials science.

Experimental Protocols

Protocol 1: Multi-Stage LLM Pipeline for Hierarchical Data Extraction

This protocol describes a methodology for extracting a comprehensive set of material features from scientific literature using a multi-stage, source-tracked approach [28].

Research Reagent Solutions

Table 2: Essential Components for the Multi-Stage LLM Pipeline

Item/Resource	Function/Description
Full-Text Scientific Articles	The primary source of unstructured data, typically in PDF format.
Large Language Model (e.g., OpenAI's o3-mini)	The core engine for text comprehension, information identification, and structured data output.
Prompt Library	A set of engineered instructions for each extraction stage (e.g., for composition, processing, microstructure).
Document Parsing Software	Converts PDF files into plain text, handling complex formatting, tables, and figures.
NoSQL Database (e.g., MongoDB)	A flexible repository for storing the extracted structured data, accommodating semi-structured and hierarchical data formats common in materials science [27].

Step-by-Step Procedure

• Text Preprocessing: Convert the target PDF articles into plain text. Clean the text to remove artifacts from PDF conversion.
• Stage 1 - Global Material Identification:
  • Prompt the LLM to analyze the entire article text and identify all reported materials alongside their fundamental attributes, primarily chemical composition and processing parameters.
  • The output is a initial list of materials that serve as the foundation for subsequent, targeted extraction stages.
• Stage 2 - Iterative Microstructure Extraction:
  • For each material identified in Stage 1, provide the LLM with the material's context (composition, processing) and the relevant text.
  • Prompt the LLM to extract detailed microstructure information (e.g., matrix phase, precipitate morphology, grain size, volume fraction).
  • Enforce Source Tracking: Require the LLM to cite the specific text passage that justifies each extracted value.
• Stage 3 - Iterative Property Extraction:
  • For the same material, provide the LLM with all accumulated context (composition, processing, microstructure) and the relevant text.
  • Prompt the LLM to extract relevant material properties (e.g., yield strength, hardness, electrical conductivity).
  • Enforce Source Tracking: Again, require citations for the source of each property value.
• Stage 4 - Database-Level Validation:
  • After processing all materials, the LLM performs a final validation check on the complete structured database against the original source texts.
  • This step leverages the retained source tracking information to systematically correct inconsistencies and validate the integrity of the extracted data.

The following workflow diagram illustrates the hierarchical and iterative nature of this protocol:

Protocol 2: ChatExtract for Property-Focused Data Extraction

This protocol utilizes a conversational LLM and a series of engineered prompts to accurately extract specific material-property datapoints, minimizing hallucinations [4].

Research Reagent Solutions

Table 3: Essential Components for the ChatExtract Protocol

Item/Resource	Function/Description
Conversational LLM (e.g., GPT-4)	An LLM that retains context and information within a single conversation session.
Sentence Tokenizer	Software to split the input text corpus into individual sentences or short passages.
Engineered Prompt Sequence	A pre-defined set of prompts for classification, extraction, and verification.

Step-by-Step Procedure

• Data Preparation and Sentence Segmentation: Gather the research papers and convert them to plain text. Split the text into individual sentences.
• Stage (A) - Relevancy Classification:
  • Prompt the LLM to classify each sentence as "relevant" or "irrelevant." A relevant sentence is one that contains a data point consisting of a Material, a Value, and a Unit for the property of interest.
  • Discard all sentences classified as irrelevant.
• Passage Construction: For each relevant sentence, create a short passage that includes the paper's title, the sentence preceding the target sentence, and the target sentence itself. This provides necessary context, such as the material name.
• Stage (B) - Data Extraction and Verification:
  • a) Single vs. Multi-Valued Determination: Prompt the LLM to determine if the passage contains a single data point or multiple data points. This dictates the subsequent path.
  • b) Extraction Path for Single-Value Passages: Use a direct prompt to extract the Material, Value, and Unit. The prompt should explicitly allow for a "Not Mentioned" response to discourage guessing.
  • c) Extraction Path for Multi-Value Passages: This is a more rigorous, multi-step process:
    • Initial Extraction: Prompt the LLM to list all data points.
    • Uncertainty-Inducing Redundancy: Ask a series of follow-up, yes/no questions that suggest uncertainty about the initial extraction (e.g., "Are you sure that [Value] [Unit] corresponds to [Material]?"). This forces the model to re-analyze the text and correct its own mistakes.
    • Structured Output: Finally, instruct the LLM to output the verified data in a strict, structured format (e.g., JSON) for easy post-processing.

The following diagram outlines the key decision points in the ChatExtract protocol:

Continuous validation represents a paradigm shift in how we ensure the reliability and accuracy of artificial intelligence (AI) models, particularly those used for automated data extraction in scientific domains. In the context of materials science research, where AI models systematically extract data such as material properties, synthesis conditions, and performance metrics from vast collections of scientific literature, continuous validation is the engineered process that maintains model integrity despite evolving data and requirements. This approach moves beyond traditional one-time validation to an ongoing, automated system of checks and balances that allows AI models to adapt without sacrificing accuracy or performance.

The fundamental challenge addressed by continuous validation is the static nature of conventional AI models when faced with a dynamic world. Materials science research is particularly fluid, with new compounds, characterization techniques, and experimental data emerging continuously. A model trained on yesterday's research papers may fail to accurately extract information from tomorrow's publications, especially when they contain novel material descriptors or experimental approaches. Continuous validation provides the necessary framework to detect these performance drifts and implement corrections in near-real-time, ensuring that data extraction remains accurate as both the AI and the scientific domain evolve [91].

The Imperative for Continuous Validation

The Challenge of Evolving AI and Data

The materials science research landscape is characterized by rapid publication rates and constantly evolving terminology, creating a moving target for AI-based data extraction systems. Unlike traditional software, AI models face performance decay not through code changes but through semantic drift in the very data they process. As noted by industry experts, "Whatever hardware you provide needs to be able to do the same. The same is true for all the LLMs. Every day there's a new LLM... continuous evolution of those neural networks needs to be built into the system" [91].

This evolution manifests in several critical challenges:

• Emergent Behaviors: As AI models scale in complexity and training data, they may exhibit unexpected capabilities or failure modes not present in smaller models. Research has found "breakthroughs" in the form of rapid, dramatic jumps in performance at some threshold scale, with the threshold varying based on the task and model [92].
• Algorithmic Evolution: The rapid pace of change in AI algorithms complicates decisions about what to implement in software and how flexible the underlying hardware needs to be [91].
• Data Contamination Risks: With the increasing appearance of AI-generated content in training and testing data, particularly synthetically generated data, validation becomes increasingly complex [92].

Limitations of Traditional Validation Approaches

Traditional validation methodologies, while effective for static models, prove insufficient for AI systems operating in dynamic research environments. Model validation in finance has a well-understood methodology and generally accepted best practices, but these approaches cannot directly translate to AI systems for several reasons:

• Scale Disparity: AI models such as LLMs are trained on about 10,000 times more data than a human ever sees, creating validation datasets several orders of magnitude greater than those used for traditional models [92].
• Dimensional Complexity: The high dimensionality of AI models means that classical asymptotic theory often fails to provide useful predictions, and standard statistical techniques can break down [92].
• Explainability Deficits: Results emerging from complex AI models are proving difficult to explain with current methods, creating challenges for validation and trust [92].

Continuous Validation Framework for Scientific Data Extraction

Core Principles

A robust continuous validation framework for scientific data extraction rests on three foundational principles:

• Automated Re-validation Cycles: Implementation of scheduled and trigger-based validation protocols that automatically test model performance against predefined benchmarks, with particular attention to emerging material classes and characterization techniques.
• Multi-layered Assessment: Following the three-tiered approach to auditing AI systems described by Mökander et al., covering model governance, and performance at both the model and application-specific levels [92].
• Adaptive Correction Mechanisms: Systems that not only identify performance degradation but automatically implement corrective actions, such as model retraining or parameter adjustment.

Implementation Architecture

The architectural foundation for continuous validation must emphasize modularity and adaptability. As noted by experts, "Whatever we build needs to be flexible enough to outlive at least the next two generations" of AI models and scientific publishing trends [91]. Key architectural components include:

• Event-Driven Security Architecture: Security systems must respond to events and changes in real-time rather than relying on periodic assessments, incorporating continuous monitoring and event-based policy enforcement [93].
• Composable Security Controls: Security systems must be modular and adaptable to accommodate unknown future agent behaviors, featuring modular policy components and extensible monitoring capabilities [93].
• Stream Processing Infrastructure: Deploying real-time data processing capabilities that can scale to support continuous agent monitoring and validation [93].

Quantitative Performance Benchmarks

Establishing clear quantitative benchmarks is essential for measuring the effectiveness of continuous validation systems. The following table summarizes key performance metrics from recent implementations of validated AI systems for scientific data extraction:

Table 1: Performance Metrics for Validated AI Data Extraction Systems

System Component	Performance Metric	Baseline Performance	Enhanced Performance with Continuous Validation
Data Extraction Accuracy	Precision/Recall	75-82% (Single Validation)	87-91% (ChatExtract Method) [4]
Model Adaptation Rate	Time to Integrate New Material Classes	2-3 Months (Manual)	2-3 Days (Automated) [91]
Error Detection	False Positive/Negative Rates	15-20% (Static Rules)	5-8% (Learning Systems) [92]
Hallucination Control	Factually Incorrect Responses	12-18% (Standard LLMs)	3-5% (Uncertainty-Inducing Prompts) [4]

These benchmarks demonstrate that continuous validation approaches can significantly enhance the reliability of AI systems for scientific data extraction. The ChatExtract method, for instance, achieved precision and recall rates both approaching 90% for materials data extraction through its sophisticated validation workflow [4].

Experimental Protocols for Validation

Protocol 1: ChatExtract Data Extraction Validation

The ChatExtract method provides a robust protocol for validating AI-powered data extraction from materials science literature, with particular effectiveness for extracting material-property triplets (Material, Value, Unit) [4].

Workflow and Process

Methodology

• Text Preparation Phase:

  • Gather research papers and remove HTML/XML syntax
  • Divide text into sentences and sentence clusters (target sentence, preceding sentence, title)
  • Apply initial keyword filters to identify potentially relevant text passages [4]

• Stage A: Initial Classification:

  • Apply simple relevancy prompt to all sentences
  • Weed out sentences that do not contain relevant data
  • Expand positive sentences to include preceding sentence and paper title for context [4]

• Stage B: Data Extraction & Verification:

  • Path B1 (Single Value): For texts containing single data points, apply direct extraction prompts for value, unit, and material
  • Path B2 (Multiple Values): For complex texts with multiple data points:
    • Apply uncertainty-inducing redundant prompts that encourage negative answers when appropriate
    • Use follow-up questions to verify extracted relationships
    • Embed all questions in a single conversation to leverage information retention [4]

• Validation Features:

  • Explicitly allow for negative answers to discourage hallucination
  • Enforce strict Yes/No format for verification questions
  • Implement redundancy through multiple questioning approaches [4]

Protocol 2: Multi-Agent AI System Validation

As AI systems evolve toward multi-agent architectures, new validation challenges emerge that require specialized protocols.

Workflow and Process

Methodology

• Adaptive Access Control Implementation:

  • Deploy intent-based security policies that understand agent objectives
  • Implement dynamic permission adjustment based on agent behavior patterns
  • Establish context-aware authorization considering agent collaboration requirements [93]

• Real-Time Agent Monitoring:

  • Track complete decision trails and data influencing agent decisions
  • Monitor how agent objectives evolve over time
  • Log inter-agent communication and collaboration patterns
  • Implement behavioral anomaly detection for identifying policy violations [93]

• Predictive Risk Assessment:

  • Develop machine learning systems that predict likely agent behaviors
  • Conduct scenario planning for potential agent actions
  • Implement proactive policy enforcement to prevent risky behaviors [93]

• Explainable Agent Governance:

  • Document why agents made specific data access decisions
  • Automatically generate compliance evidence for regulatory requirements
  • Translate complex agent behaviors into human-understandable explanations [93]

The Scientist's Toolkit: Research Reagent Solutions

Implementing continuous validation requires both computational and experimental resources. The following table details essential components for establishing a robust validation framework:

Table 2: Essential Research Reagent Solutions for Continuous Validation

Tool/Category	Specific Examples	Function in Validation	Implementation Considerations
Validation Frameworks	BIG-bench, ReLM	Provides standardized tasks for benchmarking LLM behavior	BIG-bench: 204 tasks, 450 authors, 132 institutions [92]
Data Extraction Engines	ChatExtract, Custom NLP pipelines	Automated extraction of material-property data from literature	Precision: 90.8%, Recall: 87.7% for bulk modulus [4]
Monitoring Infrastructure	Stream processing architectures, WAVE	Continuous monitoring of model performance and data quality	Real-time processing of agent behavior at scale [93]
Testing Corpora	Domain-specific text corpora, Materials science datasets	Ground truth data for validation benchmarks	Should include both single-valued and multi-valued data (70% multi-valued in bulk modulus test) [4]
Contrast Checkers	WebAIM Contrast Checker, Coolors	Ensure visualization accessibility in validation dashboards	WCAG requires 4.5:1 for normal text, 3:1 for large text [94]

Implementation Roadmap

Deploying a comprehensive continuous validation system requires phased implementation over multiple years:

• Foundation Building (2025-2026):

  • Implement comprehensive monitoring for existing AI systems
  • Deploy real-time data processing capabilities
  • Build security policy frameworks as code
  • Train teams on AI system governance [93]

• Pilot Agent Systems (2026-2027):

  • Implement pilot agentic AI systems with comprehensive monitoring
  • Deploy dynamic permission systems
  • Implement machine learning systems for anomalous behavior detection [93]

• Scale and Optimize (2027-2028):

  • Scale agentic AI systems across enterprise environments
  • Implement security for complex multi-agent systems
  • Deploy predictive risk management analytics [93]

Continuous validation represents a fundamental requirement for maintaining AI reliability in the dynamic domain of materials science research. By implementing the frameworks, protocols, and tools outlined in these application notes, research organizations can create AI systems that not only extract scientific data with high precision today but maintain that accuracy as both AI capabilities and scientific knowledge evolve. The future of AI in scientific research depends on building validation systems that are as adaptive and intelligent as the models they monitor, ensuring that our automated research tools remain trustworthy partners in scientific discovery.

Conclusion

The automation of data extraction from materials science literature marks a pivotal shift from slow, manual curation to rapid, AI-driven knowledge discovery. By leveraging a combined approach of sophisticated LLMs and specialized NLP models, researchers can now build extensive, high-quality databases that were previously impossible to assemble. This capability directly accelerates the design of novel materials and has profound implications for biomedical research, enabling faster identification of biomaterials, drug delivery systems, and diagnostic tools. Success hinges on a careful balance of methodological rigor, continuous validation, and cost optimization. As these technologies mature, their integration into the scientific workflow will become seamless, ultimately pushing the boundaries of innovation in materials science and therapeutic development.

References

• 1. KnowMat: Transforming Unstructured Material Science ... [https://chemrxiv.org/engage/chemrxiv/article-details/6852e5ecc1cb1ecda0ad8c7b]
• 2. Materials informatics: A review of AI and machine learning ... [https://www.sciencedirect.com/science/article/pii/S2352492825020379]
• 3. challenges in materials information extraction - RSC Publishing [https://pubs.rsc.org/en/content/articlehtml/2024/dd/d4dd00032c]
• 4. Extracting accurate materials data from research papers ... [https://www.nature.com/articles/s41467-024-45914-8]
• 5. How Well Do Large Language Models Understand Tables ... [https://link.springer.com/article/10.1007/s40192-024-00362-6]
• 6. Systematic Reviews: Step 7: Extract Data from Included Studies [https://guides.lib.unc.edu/systematic-reviews/extract-data]
• 7. Materials Informatics III: Polymers, Solvents and Energetic ... [https://link.springer.com/book/10.1007/978-3-031-78724-9]
• 8. Automated extraction of materials system charts using a ... [https://www.sciencedirect.com/science/article/abs/pii/S1359646225002787]
• 9. Applications of natural language processing and large ... [https://www.nature.com/articles/s41524-025-01554-0]
• 10. Named Entity Recognition and Normalization Applied to Large ... [https://eta.lbl.gov/publications/named-entity-recognition-and]
• 11. From text to insight: large language models for chemical data ... [https://pubs.rsc.org/en/content/articlehtml/2025/cs/d4cs00913d]
• 12. Large language models in materials science and the need ... [https://arxiv.org/html/2511.10673v1]
• 13. MatNexus: A comprehensive text mining and analysis suite ... [https://www.sciencedirect.com/science/article/pii/S2352711024000256]
• 14. Revolutionizing Materials Science with NLP [https://mila.quebec/en/article/revolutionizing-materials-science-with-nlp-introducing-matsci-nlp-and-honeybee]
• 15. Generating materials design hypotheses via a large ... [https://www.sciencedirect.com/science/article/abs/pii/S1359645425005932]
• 16. Foundation Models, LLM Agents, Datasets, and Tools [https://arxiv.org/html/2506.20743v1]
• 17. From Text to Insight: Large Language Models for Materials ... [https://arxiv.org/abs/2407.16867]
• 18. 14 Comparing quantitative data between individuals [https://bookdown.org/pkaldunn/SRM-Textbook/BetweenQuantData.html]
• 19. Opportunities and challenges of text mining in materials ... [https://www.sciencedirect.com/science/article/pii/S2589004221001231]
• 20. Materials Informatics 2025-2035: Markets, Strategies, Players [https://www.idtechex.com/en/research-report/materials-informatics-2025/1096]
• 21. Ten tips for a text-mining-ready article - PubMed Central [https://pmc.ncbi.nlm.nih.gov/articles/PMC7289435/]
• 22. MatSciBERT: A materials domain language model for text ... [https://www.nature.com/articles/s41524-022-00784-w]
• 23. GPT-4 [https://openai.com/index/gpt-4-research/]
• 24. Data extraction from polymer literature using large ... [https://www.nature.com/articles/s43246-024-00708-9]
• 25. Quantifying the advantage of domain-specific pre-training ... [https://www.sciencedirect.com/science/article/pii/S2666389922000733]
• 26. Article Exploring lightweight language models for materials ... [https://www.sciencedirect.com/science/article/pii/S2666386425003236]
• 27. An automatic scientific data collection framework for ... [https://www.sciencedirect.com/science/article/abs/pii/S0927025625001156]
• 28. Reliable End-to-End Material Information Extraction from ... [https://arxiv.org/html/2510.05142v1]
• 29. Machine Learning-Driven Materials Discovery [https://www.sciencedirect.com/science/article/abs/pii/S235221432500139X]
• 30. A Review on Electronic Health Record Text-Mining for ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC10178605/]
• 31. Biomedical named entity recognition using deep neural ... [https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-019-3321-4]
• 32. Dynamic In-context Learning with Conversational Models ... [https://arxiv.org/html/2405.10448v2]
• 33. Few-Shot Prompting [https://www.promptingguide.ai/techniques/fewshot]
• 34. Property Measurements from Extracting ... | PNNL Material Scientific [https://www.pnnl.gov/publications/extracting-material-property-measurements-scientific-literature-limited-annotations]
• 35. Prompt Engineering | Lil'Log [https://lilianweng.github.io/posts/2023-03-15-prompt-engineering/]
• 36. SciQu: A New Era in Material - Simple Research Science [https://scisimple.com/en/articles/2025-07-10-sciqu-a-new-era-in-material-research--ak2pr01]
• 37. Large language models for knowledge graph extraction from tables in... [https://pubs.rsc.org/en/content/articlelanding/2025/dd/d4dd00362d]
• 38. Best Text Analytics Tools and Software in 2025 [https://www.sganalytics.com/blog/text-analytics-tools-and-software/]
• 39. Tracking materials science data lineage to manage ... [https://www.nature.com/articles/s41524-019-0216-x]
• 40. A general-purpose material property data extraction ... [https://www.nature.com/articles/s41524-023-01003-w]
• 41. LLM09:2025 Misinformation - OWASP Gen AI Security Project [https://genai.owasp.org/llmrisk/llm09-overreliance/]
• 42. Mitigating Hallucination in Large Language Models (LLMs) [https://arxiv.org/html/2510.24476v1]
• 43. Evaluating and Enhancing Factual Accuracy in LLM ... [https://arxiv.org/html/2507.22940v1]
• 44. LLM Hallucinations in 2025: How to Understand and ... [https://www.lakera.ai/blog/guide-to-hallucinations-in-large-language-models]
• 45. The Real Metric That Will Decide AI Winners in 2025 [https://www.linkedin.com/pulse/llm-accuracy-real-metric-decide-ai-winners-2025-reetesh-agrawal-iswhc]
• 46. Best LLM evaluation platforms 2025 - Articles - Braintrust [https://www.braintrust.dev/articles/best-llm-evaluation-platforms-2025]
• 47. LLM Cost Optimization: The Real Patterns Behind 70% ... [https://medium.com/@techdigesthq/llm-cost-optimization-the-real-patterns-behind-70-savings-in-august-2025-7967704c121b]
• 48. LLM Cost Comparison 2025: A Deep Dive into Managing ... [https://skywork.ai/skypage/en/LLM-Cost-Comparison-2025-A-Deep-Dive-into-Managing-Your-AI-Budget/1975592241004736512]
• 49. Why LLM Cost Management is Important in 2025 [https://www.binadox.com/blog/why-llm-cost-management-is-important-in-2025/]
• 50. Data‐Driven Materials Science: Status, Challenges, and ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC6839624/]
• 51. Large language models for knowledge graph extraction from ... [https://pubs.rsc.org/en/content/articlehtml/2025/dd/d4dd00362d]
• 52. The 6 Data Quality Dimensions with Examples [https://www.collibra.com/blog/the-6-dimensions-of-data-quality]
• 53. 14 Most Common Data Quality Issues and How to Fix Them [https://lakefs.io/data-quality/data-quality-issues/]
• 54. 10 Common Data Quality Issues (And How to Solve Them) [https://firsteigen.com/blog/10-common-data-quality-issues-and-how-to-solve-them/]
• 55. History of IUPAC Polymer Terminology and Nomenclature [https://iupac.org/history-of-iupac-polymer-terminology-and-nomenclature/]
• 56. Polymer science [https://en.wikipedia.org/wiki/Polymer_science]
• 57. POLY - Nomenclature Note 13 - American Chemical Society [http://old.polyacs.org/724.html]
• 58. Polymer abbreviations [https://www.distrupol.com/polymer_abbreviations.html]
• 59. Acronyms for Plastics (PLASTICS) abbreviations list [https://www.professionalplastics.com/ACRONYMS?srsltid=AfmBOorpnvblv0I0JSrvkLlJeNq-VLMHCiZ0HQLrOIlAuwO0SSsVFumZ]
• 60. and Data - IBM Governance Compliance Research [https://research.ibm.com/projects/data-governance-and-compliance]
• 61. (PDF) data security and compliance through effective data governance [https://www.academia.edu/128747417/DATA_SECURITY_AND_COMPLIANCE_THROUGH_EFFECTIVE_DATA_GOVERNANCE]
• 62. Research Protocols - Pharmaceutical Sciences ... - Guides [https://guides.lib.uiowa.edu/c.php?g=132196&p=1087496]
• 63. Chapter 15 Experimental protocols | Lab Handbook [https://ccmorey.github.io/labHandbook/experimental-protocols.html]
• 64. Legacy System Migration: Strategy, Challenges ... [https://sam-solutions.com/blog/legacy-system-migration/]
• 65. Legacy System Migration : Common Challenges & Strategies [https://www.stromasys.com/resources/overcoming-legacy-system-migration-challenges-a-comprehensive-guide/]
• 66. Break Down Data Silos for a Connected Ecosystem [https://www.velosio.com/blog/breaking-down-data-silos-to-build-a-connected-data-ecosystem/]
• 67. Breaking Data Silos: Smarter Resource Management ... [https://www.gevernova.com/software/blog/breaking-down-data-silos-smarter-manufacturing-resource-management]
• 68. 5 Phases of Legacy System Migration + Common Strategies [https://www.superblocks.com/blog/legacy-system-migration]
• 69. MatSKRAFT: A framework for large-scale materials ... [https://arxiv.org/html/2509.10448v1]
• 70. Best Color Palettes for Scientific Figures and Data ... [https://www.simplifiedsciencepublishing.com/resources/best-color-palettes-for-scientific-figures-and-data-visualizations]
• 71. Color Contrast Accessibility: Complete WCAG 2025 Guide ... [https://www.allaccessible.org/blog/color-contrast-accessibility-wcag-guide-2025]
• 72. Color contrast - Accessibility - MDN Web Docs - Mozilla [https://developer.mozilla.org/en-US/docs/Web/Accessibility/Guides/Understanding_WCAG/Perceivable/Color_contrast]
• 73. ChatGPT vs LLaMA 2 Comparison [https://www.byteplus.com/en/topic/558629]
• 74. Llama 3 vs Llama 2 (2025): Which Model to Use? [https://kanerika.com/blogs/llama-3-vs-llama-2/]
• 75. How to Use GPT-3.5 in 2025: Still Worth Using, or Time to ... [https://blog.promptlayer.com/how-to-use-gpt-3-5-in-2025-still-worth-using-or-time-to-upgrade/]
• 76. Llama 2 Vs GPT-3.5 Vs GPT-4: What, When & How To Chose [https://www.labellerr.com/blog/llama2-vs-gpt-3-5-vs-gpt-4/]
• 77. MaScQA: investigating materials science knowledge of ... [https://www.sciencedirect.com/org/science/article/pii/S2635098X24000159]
• 78. Tested the 'top of 2025' LLMs on a real task. GPT-3.5 won. [https://www.narev.ai/blog/gpt35-beats-gpt5]
• 79. LLaMA 2 Statistics And User Trends 2025 [https://www.aboutchromebooks.com/llama-2-statistics/]
• 80. Shared metadata for data-centric materials science [https://www.nature.com/articles/s41597-023-02501-8]
• 81. Ensuring Material Integrity: A Framework for Validation in ... [https://www.labmanager.com/ensuring-material-integrity-a-framework-for-validation-in-materials-science-and-qa-qc-labs-34529]
• 82. Management of reference data in materials science and ... [https://www.sciencedirect.com/science/article/pii/S135964542500028X]
• 83. Data Accuracy in 2025: Ensure Reliable, Quality Data [https://atlan.com/data-accuracy-101-guide/]
• 84. Quantitative Data Quality Assurance, Analysis and Presentation [https://pmc.ncbi.nlm.nih.gov/articles/PMC12056448/]
• 85. Contrast Finder, find correct color contrasts for web ... [https://app.contrast-finder.org/?lang=en]
• 86. Data Science and Engineering With Human in the Loop ... [https://hdsr.mitpress.mit.edu/pub/812vijgg]
• 87. Human-in-the-loop machine learning: a state of the art [https://link.springer.com/article/10.1007/s10462-022-10246-w]
• 88. Proceedings of the Workshop on Human-In-the-Loop Data ... [https://2025.sigmod.org/toc-hilda.html]
• 89. Human-in-the-loop for Bayesian autonomous materials ... [https://www.sciencedirect.com/science/article/pii/S2590238524000067]
• 90. Comparing the accuracy of AI-assisted data extraction ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC12593456/]
• 91. Future-proofing AI Models [https://semiengineering.com/future-proofing-ai-models/]
• 92. Adapting model validation in the age of AI [https://www.deloitte.com/global/en/Industries/financial-services/perspectives/adapting-model-validation.html]
• 93. Future-Proofing Data Security: Building for Agentic AI and ... [https://www.relyance.ai/blog/future-proofing-data-security-building-agentic-ai-beyond]
• 94. Color Contrast Guidelines for Text & UI Accessibility [https://daily.dev/blog/color-contrast-guidelines-for-text-and-ui-accessibility]