Evaluating MS-ANI vs SS-ANI for Surface Hopping Simulations: A Cyclohexadiene Photochemical Ring-Opening Case Study.

Machine learning potentials (MLPs) can offer a powerful, cost-effective alternative to traditional quantum mechanical (QM) methods for simulating excited-state dynamics. In this work, we present a comprehensive comparison of two MLP models, SS-ANI and MS-ANI, by simulating the photochemical ring-opening dynamics of 1,3-cyclohexadiene (CHD). The ring-opening dynamics were investigated by using a Landau-Zener-based surface hopping algorithm. The training data sets were generated from QM-based surface hopping simulations at the SA-3-CASSCF/cc-pVDZ approach. We show that MS-ANI outperforms SS-ANI in modeling multiple electronic states, yielding more accurate energy predictions, stable energy gaps, and reliable population dynamics. MS-ANI effectively captures interstate correlations and ensures consistent energy profiles, making it robust for surface hopping dynamics. Data set quality and diversity were also found critical, with MS-ANI benefiting most from larger, well-curated data sets. These findings establish MS-ANI as a powerful, scalable approach for sufficiently accurate excited-state simulations in complex photochemical systems.