MOFGPT:使用语言模型的金属有机框架的生成设计
Srivathsan Badrinarayanan1, Rishikesh Magar2, Akshay Antony2
1Department of Chemical Engineering, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213, United States.
Journal of chemical information and modeling
|August 28, 2025
概括
我们使用强化学习和变压器模型开发了一个新的AI框架, 这种方法加速了具有特定属性的MOF的发现,克服了传统计算方法的局限性.
科学领域:
- 材料化学
- 计算材料科学
- 化学中的人工智能
背景情况:
- 发现具有定制性质的金属有机框架 (MOF) 由于其庞大的结构复杂性而具有挑战性.
- 像DFT这样的传统计算方法是准确的, 但对于大规模选来说太慢了.
- 机器学习 (ML) 为加速材料发现提供了数据驱动的替代方案.
研究的目的:
- 使用先进的人工智能技术开发新的,可扩展的MOF设计框架.
- 解决MOF复杂性在生成模型中的挑战.
- 加速发现具有所需功能属性的可合成MOF.
主要方法:
- 一个增强学习 (RL) 增强的,基于变压器的生成框架被开发出来.
- 一个化学信息的字符串表示,MOFid,用于编码MOF连接和拓.
- 该管道集成了基于GPT的发电机,变压器属性预测器 (MOFormer) 和RL优化模块.
主要成果:
- 该框架成功生成了拓有效和可合成的MOF.
- 在RL模块中以属性为导向的奖励函数优化生成的MOF候选项.
- 该方法证明了具有特定特性的MOF的有效反向设计.
结论:
- 大型语言模型与强化学习相结合可以显著加速网状化学中的反向设计.
- 这种人工智能驱动的方法为计算式MOF发现打开了新的可能性.
- MOFid表示可实现复杂材料的可扩展和有效的生成建模.
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