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High Entropy Alloys Database generated with Large Language Model.

Vladimir Chizhevskiy1,2, Gordana Marković3,4, Salah-Eddine Benrazzouq4

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Scientific Data
|March 5, 2026
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Summary
This summary is machine-generated.

Researchers analyzed 4,625 articles on high entropy alloys (HEAs) using NLP and LLMs. They created a database of 12,427 HEAs, detailing compositions and structures with high accuracy.

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Area of Science:

  • Materials Science
  • Computational Materials Science
  • Data Science

Background:

  • High entropy alloys (HEAs) are a rapidly growing field in materials science.
  • Systematic analysis of the vast HEA literature is a significant challenge.
  • Existing research data is often unstructured and difficult to access.

Purpose of the Study:

  • To develop an automated method for extracting and structuring data from HEA scientific literature.
  • To create a comprehensive database of high entropy alloys, including their compositions, phases, and structures.
  • To distinguish and catalog data from theoretical and experimental HEA studies.

Main Methods:

  • Utilized Natural Language Processing (NLP) techniques to analyze a corpus of 4,625 scientific articles.
  • Employed Large Language Models (LLMs), including mamba-transformer hybrid architectures, for data extraction.
  • Developed prompt engineering strategies to refine LLM performance for specific data points.
  • Implemented a systematic approach to differentiate and record parameters for theoretical and experimental studies.

Main Results:

  • Successfully identified and characterized 12,427 unique high entropy alloys from the analyzed literature.
  • Developed a structured database containing alloy compositions, phase numbers, and crystallographic structures.
  • Achieved high accuracy rates: 94.3% for HEA composition and 78.7% for HEA phase identification.
  • Cataloged methodological details for both theoretical (modeling approaches, computational parameters) and experimental (synthesis methods, processing conditions) studies.

Conclusions:

  • Demonstrated the feasibility of using NLP and LLMs for large-scale, automated data extraction in materials science.
  • The created database provides a valuable, structured resource for researchers in the high entropy alloy field.
  • This automated approach significantly enhances the accessibility and usability of extensive HEA research data.