(612a) Comparing Classical and Machine Learned Force Fields for Modeling Deformation of Solid Sorbents Relevant for Direct Air Capture

Direct air capture (DAC) with solid sorbents such as metal–organic frameworks (MOFs) has been widely studied. Computational materials screening can help identify promising materials from the vast chemical space of potential sorbents. Experiments have shown that MOF framework flexibility and deformation induced by adsorbate molecules can drastically affect adsorption properties such as capacity and selectivity. Force field (FF) models are commonly used as surrogates for more accurate density functional theory (DFT) calculations when modeling sorbents, but prior work with FFs for MOFs generally assume framework rigidity to simplify calculations. Although flexible FFs for MOFs have been parameterized for specific materials, it is far from clear whether general FFs can reliably model adsorbate-induced deformation to near DFT accuracy. This work benchmarks the efficacy of several general FFs at describing adsorbate-induced deformation for DAC against that from DFT. Specifically, we compare a common classical FF (UFF4MOF) with three machine learned (ML) FFs: the M3GNet interatomic potential (IAP), the MACE-MP-0 IAP, and the Equiformer V2 model developed from the recent Open DAC 2023 data set. Our results show that classical methods are insufficient for describing framework deformation, especially in cases of interest for DAC where strong interactions exist between adsorbed molecules and MOF frameworks. The emerging ML methods we tested appear to be promising for emulating the deformation behavior described by DFT at a significantly reduced computational cost.