Molecular Mechanism of Autodissociation in Liquid Water: Density Functional Theory Molecular Dynamics Simulations

26 May 2022, Version 2
This content is a preprint and has not undergone peer review at the time of posting.


Autodissociation in liquid water is one of the most important processes in various topics of physical chemistry, such as acid-base chemistry. Molecular simulations have elucidated most of the molecular mechanisms at the atomic level, yet quantitative analysis to compare with experiments using the potential of mean force (PMF) remains a hurdle, including the definition of reaction coordinates and accuracy of liquid structures by ab initio molecular dynamics (AIMD) simulations with density functional theory (DFT) methods. Here, we perform AIMD simulations with the revPBE-D3 exchange-correlation functional to compute the PMF profiles of autoionization, or proton transfer (PT), in liquid water. For the quantitative analysis with physically meaningful reaction coordinates, we employ a PT coordinate, donor-acceptor (OH--H3O+) distance, and hydrogen (H)-bond number. The one-dimensional (1D) PMF profile along the PT coordinate shows no local minimum in the product state of PT (OH- and H3O+), which is necessary to accurately compute acid dissociation constant (or pKa). On the other hand, the 2D PMF profiles along the PT coordinate and donor-acceptor distance show local minima in the product state and reaction barriers, and the computed pKw is comparable to the experiment. In addition, the 2D PMF profiles along the PT coordinate and the H-bond number reveal the molecular mechanism of the H-bond rearrangement concomitant with PT, in which the H-bond breaking before PT is slightly preferable. These findings indicate that accurate evaluation of pKa by MD simulations requires the donor-acceptor distance in addition to the conventional PT coordinate.


liquid water
proton transfer
molecular dynamics simulation
density functional theory
potential of mean force
hydrogen bond

Supplementary materials

Supporting Information
Dependence of the 2D PMF on the computational details (sampling method, basis set, system size), effects of generalized gradient approximation, analysis of Wannier centers, 2D probability distributions of reaction coordinates, convergence of PMF


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