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The paper introduces PFP/MM, a hybrid simulation method combining the PreFerred Potential (PFP) universal neural network potential with molecular mechanics (MM) to reduce computational cost for large-scale reactive simulations. This approach enables simulations of complex chemical reactions in condensed-phase systems at near-DFT accuracy with significantly improved efficiency. The method's effectiveness is demonstrated through simulations of alanine dipeptide dynamics, a nucleophilic addition reaction, and a cytochrome P450 hydroxylation reaction, all showing agreement with established results.
Reactive molecular simulations of complex chemical processes in realistic environments are now computationally tractable thanks to a hybrid neural network/molecular mechanics approach.
Universal machine-learning interatomic potentials (uMLIPs) enable reactive molecular simulations with near-DFT accuracy, yet applying them efficiently to large, realistic condensed-phase systems remains computationally demanding. Here we present PFP/MM, a hybrid approach that combines a uMLIP, PreFerred Potential (PFP), with molecular mechanics (MM) to enable both large-scale and long-time simulations that are challenging for uMLIP-only calculations. Using an alanine dipeptide in explicit water, we achieve multi-ns/day enhanced sampling and obtain a Ramachandran plot consistent with established basins. For an intramolecular nucleophilic addition reaction in a polar solvent environment, we reproduce the expected solvent-induced stabilization in the free-energy profile. We further apply the approach to a cytochrome P450 Compound I hydroxylation reaction and obtain a free-energy landscape consistent with the accepted reaction mechanism. These results demonstrate that uMLIP-based reactive simulations can be applied to diverse condensed-phase processes in large, realistic environments.