Capturing Dichotomic Solvent Behavior in Solute-Solvent Reactions with Neural Network Potentials.

ABSTRACT: Simulations of chemical reactivity in condensed phase systems represent an ongoing challenge in computational chemistry, where traditional quantum chemical approaches typically struggle with both the size of the system and the potential complexity of the reaction. Here, we introduce a workflow aimed at efficiently training neural network potentials (NNPs) to explore energy barriers in solution at the hybrid density functional theory level. The computational burden associated with training at the PBE0-D3(BJ) level is bypassed through the use of active and transfer learning techniques, whereas extensive sampling of the transition state region is accelerated by well-tempered metadynamics simulations using multiple time step integration. These NNPs serve to explore a puzzling solute-solvent re

SUBMITTER: Celerse F 

PROVIDER: S-EPMC11635972 | biostudies-literature | 2024 Dec

REPOSITORIES: biostudies-literature