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arXiv 2602.04602cond-mat.mtrl-sciphysics.data-an
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利用大语言模型自动化提取多组分合金数据用于可持续设计

Automated Extraction of Multicomponent Alloy Data Using Large Language Models for Sustainable Design

Indian Institute of Technology Madras(印度马德拉斯理工学院)

Aravindan Kamatchi Sundaram, Mohit Chakraborty, Sai Mani Kumar Devathi, B. Pabitramohan Prusty, Rohit Batra

AI总结:

利用大语言模型自动化提取多组分合金数据,构建大规模数据库并用于可持续材料选择。

摘要:

可持续材料的设计需要以组织、结构和自动化的方式从文献库中获取材料性能和可持续性数据。自然语言处理方法,特别是大语言模型(LLM),已被探索用于从文献中提取材料数据,但往往存在精度有限或范围狭窄的问题。在本工作中,开发了一个基于LLM的流程,以准确地从文献中的文本描述和表格数据中提取与合金相关的信息。具体来说,两个数据库分别包含37,711和148,069条记录;一个来自文献文本,包含合金成分、加工条件、表征方法和报告的性能;另一个来自文献表格,包含属性名称、值和单位。该流程通过提示工程和检索增强生成增强了材料领域的敏感性,并实现了文本提取的F1分数为0.83,表格提取的F1分数为0.88,超过了或匹配了现有方法。将该流程应用于超过10,000篇文章,生成了目前最大的公开多组分合金数据库,并揭示了成分和加工-性能趋势。该数据库进一步用于三个应用领域中的可持续材料选择,即轻量化、软磁和抗腐蚀,识别出具有更可持续生产过程的多组分合金候选者,同时保持或超过基准性能。开发的流程可以轻松推广到其他材料类别,并帮助开发全面、准确和可用的可持续材料设计数据库。

英文摘要:

The design of sustainable materials requires access to materials performance and sustainability data from literature corpus in an organized, structured and automated manner. Natural language processing approaches, particularly large language models (LLMs), have been explored for materials data extraction from the literature, yet often suffer from limited accuracy or narrow scope. In this work, an LLM-based pipeline is developed to accurately extract alloy-related information from both textual descriptions and tabular data across the literature on high-entropy (or multicomponent) alloys (HEA). Specifically two databases with 37,711 and 148,069 entries respectively are retrieved; one from the literature text, consisting of alloy composition, processing conditions, characterization methods, and reported properties, and other from the literature tables, consisting of property names, values, and units. The pipeline enhances materials-domain sensitivity through prompt engineering and retrieval-augmented generation and achieves F1-scores of 0.83 for textual extraction and 0.88 for tabular extraction, surpassing or matching existing approaches. Application of the pipeline to over 10,000 articles yields the largest publicly available multicomponent alloy database and reveals compositional and processing-property trends. The database is further employed for sustainability-aware materials selection in three application domains, i.e., lightweighting, soft magnetic, and corrosion-resistant, identifying multicomponent alloy candidates with more sustainable production while maintaining or exceeding benchmark performance. The pipeline developed can be easily generalized to other class of materials, and assist in development of comprehensive, accurate and usable databases for sustainable materials design.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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