MOF Synthesis Prediction Enabled by Automatic Data Mining and Machine Learning

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 .

Automated summary

This study demonstrates the potential of machine learning in predicting the synthesis parameters of metal-organic frameworks (MOFs). By establishing a MOF synthesis database and training machine learning models, the researchers achieved good prediction performance in synthesizing new MOF structures, surpassing human expert predictions.