域训练语言模型用于反向设计和合成高性能储存MOFs
Zhimeng Liu1, Yuqiao Su1, Hao Wang1
1Beijing Key Laboratory of Function Materials for Molecule & Structure Construction, School of Materials Science and Engineering, University of Science and Technology Beijing, Beijing, 100083, P.R. China.
Angewandte Chemie (International ed. in English)
|November 10, 2025
概括
一个新的AI模型,MOFs-LLM,加速了用于储存的金属有机框架 (MOF) 的发现. 它成功设计和合成了一种具有高吸收的新型MOF (Cu-LLMs-1),弥合了计算设计和实验验证.
科学领域:
- 材料科学 材料科学 材料科学
- 计算化学的计算化学
- 化学工程是化学工程的重要组成部分.
背景情况:
- 开发金属有机框架 (MOF) 等先进材料对于高效的储存至关重要.
- 传统的MOF发现方法往往耗时且资源密集.
- 改善结构-财产关系是设计具有增强性能的MOF的关键.
研究的目的:
- 开发一个特定于领域的大型语言模型 (MOFs-LLM),以加速MOFs的反向设计和合成.
- 增强模型对MOFs结构-属性关系的推理能力.
- 为了使MOF的设计具有优化的储能容量和合成可访问性.
主要方法:
- 从MOF出版物和晶体结构中训练有素的MOFs-LLM使用了2.10亿个令牌的大数据集.
- 将化学知识和结构特征整合到语言模型中.
- 通过将其结构-属性推理与基线方法进行比较,验证了模型的性能.
- 使用MOFs-LLM进行候选MOF结构的反向设计.
- 在模型预测的指导下合成了一种新的MOF.
主要成果:
- 与基线方法相比,MOFs-LLM在捕捉结构-属性关系方面表现出46.7%的增强.
- 成功设计了60个候选MOF框架,优化了的储存和合成.
- 在三次实验代中合成了一种新的MOF,Cu-LLMs-1.
- 合成的MOF在室温下显示了1.33%的吸收量,在最纯的MOF中排名第一.
- 该研究成功地将虚拟选与材料发现中的实验实现相结合.
结论:
- 像MOFs-LLM这样的域训练语言模型可以显著加速材料发现.
- MOFs-LLM显示了功能性材料的反向设计和合成的巨大潜力.
- Cu-LLMs-1的成功合成验证了该模型的预测能力和实际适用性.
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