使用大语言模型生成的高透合金数据库
Vladimir Chizhevskiy1,2, Gordana Marković3,4, Salah-Eddine Benrazzouq4
1Jožef Stefan Institute, Department of Gaseous Electronics, Jamova cesta 39, 1000, Ljubljana, Slovenia.
Scientific data
|March 5, 2026
概括
研究人员使用NLP和LLMs分析了4625篇关于高合金 (HEAs) 的文章. 他们创建了一个12427个HEA的数据库,详细描述了高精度的组成和结构.
科学领域:
- 材料科学 材料科学 材料科学
- 计算材料科学科学 计算材料科学
- 数据科学数据科学数据科学
背景情况:
- 高合金 (HEAs) 是材料科学中快速发展的一个领域.
- 对广的HEA文献进行系统分析是一个重大挑战.
- 现有的研究数据往往是无结构的,难以获取.
研究的目的:
- 开发一种自动化方法,从HEA科学文献中提取和结构化数据.
- 创建一个全面的高合金数据库,包括它们的成分,相和结构.
- 从理论和实验HEA研究中区分和目录数据.
主要方法:
- 使用自然语言处理 (NLP) 技术分析了4625篇科学文章.
- 采用大型语言模型 (LLM),包括mamba-transformer混合架构,用于数据提取.
- 开发了快速工程策略,以针对特定数据点完善LLM性能.
- 实施了一种系统的方法来区分和记录理论和实验研究的参数.
主要成果:
- 从分析文献中成功识别和表征了12,427种独特的高合金.
- 开发了一个结构化数据库,包含合金成分,相数和晶体结构.
- 实现了高准确率:HEA组成的94.3%,HEA阶段识别的78.7%.
- 对理论研究 (建模方法,计算参数) 和实验研究 (合成方法,处理条件) 的方法详细说明.
结论:
- 证明了使用NLP和LLM用于材料科学中大规模自动数据提取的可行性.
- 创建的数据库为高合金领域的研究人员提供了有价值的,结构化的资源.
- 这种自动化方法显著提高了广泛的HEA研究数据的可访问性和可用性.
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