Solvent-Inclusive ML/MM Simulations: Assessments of Structural, Dynamical, and Thermodynamic Accuracy

Chemical reactions in solution are central to biological function, synthetic chemistry, and materials design. Accurate modeling of these systems is essential for obtaining mechanistic insights, but remains computationally demanding. Hybrid machine-learned/molecular mechanics (ML/MM) simulations offer a promising compromise between quantum accuracy and computational efficiency. However, most existing ML/MM methods exclude solvent molecules from the ML region, limiting their ability to capture solvent-mediated reactivity. In this study, we introduce a solvent-inclusive ML/MM methodology that addresses this limitation. Our approach defines the ML and MM regions using a fixed spatial boundary, avoiding costly topology updates, and handles interactions across the boundary using a force-partitioning scheme. We evaluate our framework through simulations of bulk water and the solvent-mediated dissociation of formic acid using different ML region sizes. Our results reveal that structural and dynamical properties of bulk water are preserved for sufficiently large ML regions. Notably, we show that the choice of the potential energy surface representation for the ML and MM regions can affect these properties, especially at smaller ML region sizes. Our calculations of the free energy profiles for formic acid dissociation also show trends consistent with reference systems, but deviations are observed. We comment on potential reasons for these observations. Overall, our study highlights the promise of ML/MM approaches that are solvent-inclusive and provides directions for further development.