ArticleAdvanced science (Weinheim, Baden-Wurttemberg, Germany)2026
Automated Extraction of Multicomponent Alloy Data Using Large Language Models for Sustainable Design.
Article in Advanced science (Weinheim, Baden-Wurttemberg, Germany), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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5 authors.
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Abstract
The design of sustainable materials requires access to materials performance and sustainability data from literature corpus in an organized, structured, and automated manner. Large language models (LLMs) have been explored for materials data extraction from the literature, yet often suffer from limited accuracy or narrow scope. Here, we present an LLM-based pipeline for accurate information extraction from high-entropy (multicomponent) alloy (HEA) literature. The approach produces two databases with 37,711 and 148,069 entries: one from text, capturing alloy compositions, processing conditions, characterization methods, and properties; and another from tables, containing property names, values, and units. Using prompt engineering and retrieval-augmented generation, the pipeline improves materials-domain sensitivity and achieves F1-scores of
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