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Automated Extraction of Multicomponent Alloy Data Using Large Language Models for Sustainable Design
Aravindan Kamatchi Sundaram1, Mohit Chakraborty1, Sai Mani Kumar Devathi1
1Department of Metallurgical and Materials Engineering, Indian Institute of Technology Madras, Chennai, India.
Summary
A new pipeline using large language models (LLMs) accurately extracts data from high-entropy alloy literature, creating the largest public database for sustainable materials design.
Area of Science:
- Materials Science
- Computational Materials Science
- Data Science
Background:
- Sustainable materials design necessitates organized access to performance and sustainability data.
- Existing data extraction methods using large language models (LLMs) often lack accuracy and scope.
- High-entropy alloys (HEAs) present unique challenges for data extraction due to their complex compositions.
Purpose of the Study:
- To develop an LLM-based pipeline for accurate and automated information extraction from HEA literature.
- To create comprehensive databases of HEA compositions, processing, characterization, and properties.
- To support sustainability-aware materials selection and design.
Main Methods:
- An LLM-based pipeline employing prompt engineering and retrieval-augmented generation was developed.
- The pipeline extracts data from both text and tables within scientific articles.
- Databases were generated from over 10,000 HEA articles, containing millions of entries.
Main Results:
- Achieved high F1-scores (~0.83 for text, ~0.88 for tables), surpassing existing methods.
- Created the largest publicly available database for multicomponent alloys.
- Identified compositional and processing-property trends and potential sustainable alloy candidates for specific applications.
Conclusions:
- The developed pipeline enables accurate and efficient data extraction for sustainable materials design.
- The generated HEA database facilitates the discovery of novel materials with improved sustainability.
- The methodology is generalizable to other material classes, advancing materials informatics.
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