Neural Network Potential with Multiresolution Approach Enables Accurate Prediction of Reaction Free Energies in Solution.

ABSTRACT: We present the design and implementation of a novel neural network potential (NNP) and its combination with an electrostatic embedding scheme, commonly used within the context of hybrid quantum-mechanical/molecular-mechanical (QM/MM) simulations. Substitution of a computationally expensive QM Hamiltonian by an NNP with the same accuracy largely reduces the computational cost and enables efficient sampling in prospective MD simulations, the main limitation faced by traditional QM/MM setups. The model relies on the recently introduced anisotropic message passing (AMP) formalism to compute atomic interactions and encode symmetries found in QM systems. AMP is shown to be highly efficient in terms of both data and computational costs and can be readily scaled to sample systems involving more th

SUBMITTER: Pultar F 

PROVIDER: S-EPMC11869291 | biostudies-literature | 2025 Feb

REPOSITORIES: biostudies-literature