Applications of machine learning in metal-organic frameworks

Abstract

Machine learning (ML) is the field of computer science where computing systems are trained to perform an analysis of provided data to reveal previously unseen trends and patterns that allow accurate predictions. ML methods have drastically transformed the way scientific research is conducted, making significant contributions in a variety of research fields ranging from natural language processing to drug discovery and materials design. With an abundance of discovered structures and their performance data for various application fields, metal-organic frameworks (MOFs) would undoubtedly benefit from the integration of ML methods for their design and development. In this review, we provide a complete overview of how ML methods can be effectively utilized for MOF research. Various descriptors and representations of MOFs suitable for the ML workflow are first discussed. Then, recent research progresses in which novel ML methods are used to predict various material properties or even design new MOF structures are presented. As many more MOFs are discovered and utilized for various applications, ML will play a much bigger role in their research and development. As such, this review aims to provide readers with basic insights required to comprehend ML-based MOF research, and to help conduct those of their own in the future.