Towards accurate and scalable high-throughput MOF adsorption screening: merging classical force fields and universal machine learned interatomic potentials.

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.