20.500.12556/DiRROS-29238 High entropy alloys database generated with large language model High entropy alloys (HEAs) represent a promising area in materials science, but systematic analysis of the extensive literature remains a challenge. In this study, we used Natural Language Processing (NLP) techniques to analyze 4,625 scientific articles from a restricted corpus representing publisher-accessible literature, successfully identifying and characterizing 12,427 of different high entropy alloys. Through prompt engineering and experiments with Large Language Models (LLMs), including mamba-transformer hybrid architectures, we developed a structured database that captures important parameters such as alloy compositions, phase numbers and crystallographic structures. In our analysis, we distinguish between theoretical and experimental studies, considering specific methodological details for each category. For theoretical work, we have systematically documented modeling approaches and key computational parameters, while experimental studies are cataloged with their synthesis methods and critical processing conditions. This database represents a large-scale, automated extraction of HEA research data. The accuracy of the data ranges from 78.7% for HEA phase identification to 94.3% for HEA composition. high-entropy alloys natural language processing materials informatics visokokonfuzijske zlitine obdelava naravnega jezika informatika materialov true false true Nature Publishing Group Združeno kraljestvo Angleški jezik Slovenski jezik © The Author(s) 2026 Neznano 2026-04-30 11:59:21 2026-04-30 11:59:22 2026-05-01 03:46:23 0000-00-00 00:00:00 2026 0 0 Nasl. z nasl. zaslona; Soavtorji iz Slovenije: Uroš Cvelbar, Janez Zavašnik, Alexandre Nominé; Opis vira z dne 13. 3. 2026; str. [1-8] [article no.] 612 Vol. 13 Mar. 2026 0000-00-00 Zaloznikova Objavljeno apc 2025-08-28 2026-02-17 2026-04-16 620.1/.2 2052-4463 10.1038/s41597-026-06930-z 271580163 https://www.nature.com/articles/s41597-026-06930-z 1 464a8778-447b-11f1-a4de-001a4af901a5 https://dirros.openscience.si/Dokument.php?lang=slv&id=43742 RAZ_Chizhevskiy_Vladimir_2026.pdf RAZ_Chizhevskiy_Vladimir_2026.pdf 1 5A05EBDAFB4B35107FCA04A3D962E91B d9a25011a400ba3a6a7b57b31a7fa91eebb312146216993e9646a3bf9f60637c d6fea5af-447b-11f1-a4de-001a4af901a5 https://dirros.openscience.si/Dokument.php?lang=slv&id=43743 Institut Jožef Stefan 0 0 0