Towards Accurate and Scalable High-throughput MOF Adsorption Screening: Merging Classical Force Fields and Universal Machine Learned Interatomic Potentials

Abstract

High-throughput computational screening (HTCS) of gas adsorption in metal–organic frameworks (MOFs) typically relies on classical generic force fields which are computationally efficient but often fail to capture complex host–guest interactions. Universal machine learned interatomic potentials (u-MLIPs) offer near-quantum accuracy at far lower cost than density-functional theory (DFT), yet their large-scale application in adsorption screening remains limited. Here, we introduce an efficient hybrid screening workflow that merges classical generic force fields and u-MLIPs within a Monte Carlo scheme to accurately assess the adsorption performance of a large MOF structure database. As a proof-of-concept, this HTCS is applied to identify top performing MOFs for selective ethylene capture under humid conditions, highly relevant to food-preservation packaging technologies. Our workflow demonstrates that accurate treatment of both host–guest energetics and framework flexibility, enabled by u-MLIPs, is essential to achieve reliable adsorption-performance rankings.