Neural network potentials for accelerated metadynamics of oxygen reduction kinetics at Au-water interfaces.

ABSTRACT: The application of ab initio molecular dynamics (AIMD) for the explicit modeling of reactions at solid-liquid interfaces in electrochemical energy conversion systems like batteries and fuel cells can provide new understandings towards reaction mechanisms. However, its prohibitive computational cost severely restricts the time- and length-scales of AIMD. Equivariant graph neural network (GNN) based accurate surrogate potentials can accelerate the speed of performing molecular dynamics after learning on representative structures in a data efficient manner. In this study, we combined uncertainty-aware GNN potentials and enhanced sampling to investigate the reactive process of the oxygen reduction reaction (ORR) at an Au(100)-water interface. By using a well-established active learning

SUBMITTER: Yang X 

PROVIDER: S-EPMC10074416 | biostudies-literature | 2023 Apr

REPOSITORIES: biostudies-literature