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Probing the self-ionization of liquid water with ab initio deep potential molecular dynamics.
- Source :
-
Proceedings of the National Academy of Sciences of the United States of America [Proc Natl Acad Sci U S A] 2023 Nov 14; Vol. 120 (46), pp. e2302468120. Date of Electronic Publication: 2023 Nov 06. - Publication Year :
- 2023
-
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 <subscript>2</subscript> O) cells. The reversible work to separate the hydroxide and hydronium to a distance [Formula: see text] is found to converge for simulation cells containing more than 500 H <subscript>2</subscript> O, and a distance of [Formula: see text] 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.
Details
- Language :
- English
- ISSN :
- 1091-6490
- Volume :
- 120
- Issue :
- 46
- Database :
- MEDLINE
- Journal :
- Proceedings of the National Academy of Sciences of the United States of America
- Publication Type :
- Academic Journal
- Accession number :
- 37931100
- Full Text :
- https://doi.org/10.1073/pnas.2302468120