Recent advances in computational modeling of MOFs: From molecular simulations to machine learning

The reticular chemistry of metal-organic frameworks (MOFs) allows for the generation of an almost boundless number of materials some of which can be a substitute for the traditionally used porous mate-rials in various fields including gas storage and separation, catalysis, drug storage and delivery. The num-ber of MOFs and their potential applications are growing so quickly that, when novel MOFs are synthesized, testing them for all possible applications is not practical. High-throughput computational screening approaches based on molecular simulations of materials have been widely used to investigate MOFs and identify the optimal MOFs for a specific application. Despite the growing computational resources, given the enormous MOF material space, computational identification of promising MOFs requires more efficient approaches in terms of time and effort. Leveraging data-driven science techniques can offer key benefits such as accelerated MOF design and discovery pathways via the establishment of machine learning (ML) models and interpretation of complex structure-performance relationships that can reach beyond expert intuition. In this review, we present key scientific breakthroughs that propelled computational modeling of MOFs and discuss the state-of-the-art approaches extending from molecular simulations to ML algorithms. Finally, we provide our perspective on the potential opportunities and challenges for the future of big data-driven MOF design and discovery. (c) 2023 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

Automated summary

The reticular chemistry of MOFs enables the generation of countless materials, some of which can replace traditional porous materials in various fields. High-throughput computational screening approaches based on molecular simulations are used to identify optimal MOFs. However, more efficient methods are still needed due to the rapidly growing number of MOFs.