Machine Learning Accelerated Finite-Field Simulations for Electrochemical Interfaces.

ABSTRACT: Electrochemical interfaces are of fundamental importance in electrocatalysis, batteries, and metal corrosion. Finite-field methods are one of the most reliable approaches for modeling electrochemical interfaces in complete cells under realistic constant-potential conditions. However, previous finite-field studies have been limited to either expensive ab initio molecular dynamics or less accurate classical descriptions of electrodes and electrolytes. To overcome these limitations, we present a machine learning-based finite-field approach that combines two neural network models: one predicts atomic forces under applied electric fields, while the other describes the corresponding charge response. Both models are trained entirely on first-principles data without employing any classical approxi

SUBMITTER: Feng C 

PROVIDER: S-EPMC12728653 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature