Probing the self-ionization of liquid water with ab initio deep potential molecular dynamics.

ABSTRACT: The chemical equilibrium between self-ionized and molecular water dictates the acid-base chemistry in aqueous solutions, yet understanding the microscopic mechanisms of water self-ionization remains experimentally and computationally challenging. Herein, Density Functional Theory (DFT)-based deep neural network (DNN) potentials are combined with enhanced sampling techniques and a global acid-base collective variable to perform extensive atomistic simulations of water self-ionization for model systems of increasing size. The explicit inclusion of long-range electrostatic interactions in the DNN potential is found to be crucial to accurately reproduce the DFT free energy profile of solvated water ion pairs in small (64 and 128 H₂O) cells. The reversible work to separate the hydrox

SUBMITTER: Calegari Andrade M 

PROVIDER: S-EPMC10655216 | biostudies-literature | 2023 Nov

REPOSITORIES: biostudies-literature