Nanoporous materials including zeolites, metal-organic frameworks, and covalent organic frameworks offer high tunability and surface area, making them ideally suited to address global challenges such as CO2 capture and conversion, utilization of renewable feedstocks, and air purification. Molecular modeling is essential to enable atomic-scale design for optimal performance. Chemical transformations in these materials include not only catalytic reactions, but also local or global structural rearrangements and are strongly dependent on extreme operating conditions typical for industrial processes. The performance of industrial catalysts is governed by complex reaction networks and multiscale phenomena like diffusion and reactions, spanning a broad range of timescales and length scales. Recent advances at the intersection of quantum mechanics, statistical physics, and machine learning have significantly improved our ability to model complex chemical transformations in industrial catalysts and nanoporous materials. Herein, we review current modeling strategies and highlight future directions for predictive, multiscale simulations of nanoporous catalysts under realistic conditions.
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