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

Marcos Calegari Andrade, Roberto Car, Annabella Selloni

Research output: Contribution to journalArticlepeer-review

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 hydroxide and hydronium to a distance S is found to converge for simulation cells containing more than 500 H2O, and a distance of ∼ 8 Å is the threshold beyond which the work to further separate the two ions becomes approximately zero. The slow convergence of the potential of mean force with system size is related to a restructuring of water and an increase of the local order around the water ions. Calculation of the dissociation equilibrium constant illustrates the key role of long-range electrostatics and entropic effects in the water autoionization process.

Original languageEnglish (US)
Article numbere2302468120
JournalProceedings of the National Academy of Sciences of the United States of America
Volume120
Issue number46
DOIs
StatePublished - 2023

All Science Journal Classification (ASJC) codes

  • General

Keywords

  • enhanced sampling
  • liquid water
  • molecular dynamics
  • neural-network potentials
  • self-ionization

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