Organometallic Chemistry

MOF Synthesis Prediction Enabled by Automatic Data Mining and Machine Learning

Authors

Abstract

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.

Content

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Supplementary material

Thumbnail image of SI_MOF_Synthesis_Quiz.pdf
SI Synthesis Quiz
MOF synthesis expert quiz
Thumbnail image of Supporting Information.pdf
SI
General supporting information

Supplementary weblinks

GitHub: MOF Literature Extraction
GitHub repository for MOF literature extraction, and the SynMOF database.
GitHub: MOF Synthesis Prediction
GitHub repository for MOF synthesis prediction, including training data, i.e. the SynMOF database.