Machine-learning-assisted high-throughput computational screening of high performance metal–organic frameworks

Abstract

Over the past two decades, the number of works on metal–organic frameworks (MOFs) in the fields of gas adsorption and separation has experienced explosive growth due to their high void fraction and ultra-high specific surface area. With the rapid increase of MOF databases, high-throughput computational screening (HTCS) has become the main method for selecting high-performance target materials from the large quantity of MOFs. Traditional HTCS methods, e.g. grand canonical Monte Carlo (GCMC) and density functional theory (DFT), could accelerate the discovery of materials; however, there are some shortcomings in these methods such as high computational cost and slow speed, considering the vast and almost infinite MOF database as well as different separation systems and diverse operating conditions. Machine learning (ML) is a potential screening method with the ability to accurately predict the high-performance materials through the training of data, which were obtained by HTCS, and the ML model that fits accurately the complex system can improve the screening speed by 2–3 orders of magnitude. In this work, in view of ML-assisted HTCS of MOFs in recent years, the relevant research progress including CH₄ storage, H₂ storage, CO₂ separation, etc. is summarized, aiming to clarify the potential problems and challenges about ML-assisted HTCS by categorizing the application and development of ML in this field. Then, a series of ML algorithms were designed and developed to adapt to different MOF systems, and to search key descriptors based on ML to reverse design new MOFs with excellent performance. Therefore, the ML-assisted HTCS method could accelerate the development of MOFs and promote their applications in various fields.