Mechanistic insights into water autoionization

TL;DR

This study uses advanced machine learning-enhanced simulations to reveal the detailed mechanism of water autoionization, including the dual-presolvation process and asynchronous proton transfer, aligning well with experimental constants.

Contribution

It introduces a novel approach combining neural network potentials with metadynamics to accurately simulate water autoionization at the molecular level.

Findings

Reproduces water's equilibrium constant (pKw=14.14) and ionization rate constant (1.566×10⁻³ s⁻¹) accurately.

Identifies the asynchronous triple proton transfer process in autoionization.

Proposes a dual-presolvation mechanism involving hypercoordinated and undercoordinated water molecules.

Abstract

Water autoionization plays a critical role in determining pH and properties of various chemical and biological processes occurring in the water mediated environment. The strikingly unsymmetrical potential energy surface of the dissociation process poses a great challenge to the mechanistic study. Here, we demonstrate that reliable sampling of the ionization path is accessible through nanosecond timescale metadynamics simulation enhanced by machine learning of the neural network potentials with ab initio precision, which is proved by quantitatively reproduced water equilibrium constant (p$K_\mathrm{w}$=14.14) and ionization rate constant (1.566$\times10^{-3}$ s$^{-1}$). Statistical analysis unveils the asynchronous character of the concerted triple proton transfer process. Based on conditional ensemble average calculations, we propose a dual-presolvation mechanism, which suggests that a pair of hypercoordinated and undercoordinated waters bridged by one \ce{H2O} cooperatively constitutes the initiation environment for autoionization, and contributes majorly to the local electric field fluctuation to promote water dissociation.