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使用机器学习和数据挖掘来利用社区知识来设计稳定的金属有机框架

Aditya Nandy^1,2, Chenru Duan^1,2, Heather J Kulik¹

¹Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States.

Journal of the American Chemical Society

|October 13, 2021

概括

研究人员开发了机器学习模型来预测金属有机框架 (MOF) 的稳定性. 这种方法加速了用于催化和气体分离的强MOF材料的发现.

科学领域:

材料科学
计算化学
化学工程

背景情况:

金属有机框架 (MOF) 提供可调节的活性点和多孔结构,有利于气体分离和催化等应用.
对MOF稳定性的有限理解阻碍了它们的实际实施,特别是它们对热降解和溶剂去除的抵抗.

研究的目的:

在实际应用中克服MOF稳定性的局限性.
根据它们的化学和几何结构,开发MOF稳定性的预测模型.
确定增强MOF,特别是含有3D过渡金属的稳定性的策略.

主要方法:

使用自然语言处理和图像分析从数千份已发表的手稿中提取稳定性数据 (溶剂去除和热降解).
分析了MOF的大数据集中的结构属性关系.
训练有素的机器学习模型 (高斯过程,人工神经网络) 使用基于图形和孔隙结构的表示来预测MOF稳定性.

主要成果:

从近4000个手稿中获得了超过2000个溶剂去除稳定性测量和3000个热降解温度.
确定了用于MOF稳定性预测的现有启发性的局限性.
开发了能够比传统方法更快地预测MOF稳定性的ML模型.
通过ML模型解释获得了影响MOF稳定性的关键特征.

结论:

机器学习模型可以准确预测MOF的稳定性,显著加速材料的发现.
开发的方法提供了用于催化应用的MOF增加稳定性的策略.
预计这项工作将加速用于各种实际应用的稳定MOF材料的开发.

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