材料大脑:通过人-人工智能策划的几拍大语言模型来提取高性能材料合成
Yi Yang1, Zhimeng Liu2, Weize Wu1
1School of Computer Science, Beihang University, Beijing 100191, China.
Journal of chemical information and modeling
|December 25, 2025
概括
材料大脑优化大型语言模型 (LLM) 来提取金属有机框架 (MOF) 合成路径. 这个AI管道增强了MOF设计和材料性能,超越了现有的方法.
科学领域:
- 材料科学 材料科学 材料科学
- 人工智能的人工智能
- 化学工程是化学工程的重要组成部分.
背景情况:
- 提取金属有机框架 (MOF) 的合成路径对于设计功能性材料至关重要.
- 传统的方法与化学文献的复杂性和体积作斗争.
- 大型语言模型 (LLM) 提供了一个潜在的解决方案,但往往缺乏专业知识或是昂贵的微调.
研究的目的:
- 引入MaterialBrain管道,使用优化的一些射击LLMs精确提取MOF合成路径.
- 通过人工智能驱动的洞察来改进新型MOF的设计和性能.
- 解决材料科学数据提取中零射击和微调的LLM的局限性.
主要方法:
- 开发了一种基于批次时代代的人类-AI数据策划方法,以提高注释质量和数量.
- 实施了信息检索算法,以选择最佳的几次演示来进行上下文学习.
- 利用优化的一些射击LLM用于合成路径提取,结构推断和材料设计.
主要成果:
- 材料大脑在合成提取,结构推断和材料设计方面显著优于零射击的LLM和基线方法.
- 该管道在从大量MOF数据集中提取合成路径方面表现出高精度.
- 由MaterialBrain指导的实验室合成材料的特定表面积超过了文献中可比的MOF的99.2%.
结论:
- 材料大脑管道为提取MOF合成信息提供了有效和高效的解决方案.
- 优化的一些射击LLM代表了推进理性MOF设计和发现的强大工具.
- 这种方法有助于创建具有定制功能的高性能材料.
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