Probing the self-ionization of liquid water with ab initio deep potential molecular dynamics
Marcos F. Calegari Andrade, Roberto Car, Annabella Selloni
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 H 2 O) cells. The reversible work to separate the hydroxide and hydronium to a distance <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" overflow="scroll"> <mml:mi>S</mml:mi> </mml:math> is found to converge for simulation cells containing more than 500 H 2 O, and a distance of <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" overflow="scroll"> <mml:mo>∼</mml:mo> </mml:math> 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.