On the shoulders of high-throughput computational screening and machine learning: design and discovery of MOFs for H2 storage and purification

Abstract

Hydrogen (H2) is a promising energy carrier for achieving net zero carbon emissions. Metal organic frameworks (MOFs) and covalent organic frameworks (COFs) have emerged as strong alternatives to traditional porous materials for highly efficient H2 storage and purification applications. With the very rapid and continuous increase in the number and variety of MOFs and COFs, early studies in this field focused on experimental testing of a few types of randomly selected materials have recently evolved into studies combining computational screening of very large material databases with machine learning (ML). In this review, we highlighted the recent trends in merging molecular modeling and ML in the field of MOFs and COFs for H2 storage and purification. After reviewing high-throughput computational screening studies aiming to determine the best material candidates for H2 adsorption and separation, we discussed the recent studies that use ML for extracting hidden structure-performance relations from molecular simulation results to provide new guidelines for the inverse design of novel MOFs. Finally, we addressed the current opportunities and challenges of fusing data science into molecular modeling to speed the development of innovative adsorbent and membrane materials for H2 storage and separation, respectively.