Institute of Functional Interfaces, Karlsruhe Institute of Technology, Hermann-von-Helmholtz-Platz 1, 76344, Eggenstein-Leopoldshafen, Germany.
Institute of Nanotechnology, Karlsruhe Institute of Technology, Hermann-von-Helmholtz-Platz 1, 76344, Eggenstein-Leopoldshafen, Germany.
Angew Chem Int Ed Engl. 2022 May 2;61(19):e202200242. doi: 10.1002/anie.202200242. Epub 2022 Mar 10.
Despite rapid progress in the field of metal-organic frameworks (MOFs), the potential of using machine learning (ML) methods to predict MOF synthesis parameters is still untapped. Here, we show how ML can be used for rationalization and acceleration of the MOF discovery process by directly predicting the synthesis conditions of a MOF based on its crystal structure. Our approach is based on: i) establishing the first MOF synthesis database via automatic extraction of synthesis parameters from the literature, ii) training and optimizing ML models by employing the MOF database, and iii) predicting the synthesis conditions for new MOF structures. The ML models, even at an initial stage, exhibit a good prediction performance, outperforming human expert predictions, obtained through a synthesis survey. The automated synthesis prediction is available via a web-tool on https://mof-synthesis.aimat.science.
尽管金属-有机骨架（MOFs）领域取得了快速进展，但利用机器学习（ML）方法来预测 MOF 合成参数的潜力尚未被开发。在这里，我们展示了如何通过直接根据 MOF 的晶体结构来预测 MOF 的合成条件，从而将 ML 用于合理化和加速 MOF 的发现过程。我们的方法基于：i）通过从文献中自动提取合成参数来建立第一个 MOF 合成数据库，ii）通过使用 MOF 数据库来训练和优化 ML 模型，以及 iii）预测新 MOF 结构的合成条件。即使在初始阶段，ML 模型也表现出良好的预测性能，优于通过合成调查获得的人类专家预测。通过在 https://mof-synthesis.aimat.science 上的网络工具可以实现自动化的合成预测。
