论文标题
对水自动化的机械洞察力
Mechanistic insights into water autoionization
论文作者
论文摘要
水自动因子在确定水中介导的环境中发生的各种化学和生物过程的pH和性质中起着至关重要的作用。解离过程的惊人的不对称势能表面对机械研究构成了巨大挑战。 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} $）。统计分析揭示了协同三重质子转移过程的异步特征。根据条件合奏的平均计算，我们提出了一种双重启发机制，这表明一对由一个由一个\ ce {H2O}桥接的高度协调的和不符合的水，构成了自动离子的起始环境，并主要构成了当地电场促进水流解离的当地电场。
Water autoionization plays a critical role in determining pH and properties of various chemical and biological processes occurring in the water mediated environment. The strikingly unsymmetrical potential energy surface of the dissociation process poses a great challenge to the mechanistic study. Here, we demonstrate that reliable sampling of the ionization path is accessible through nanosecond timescale metadynamics simulation enhanced by machine learning of the neural network potentials with ab initio precision, which is proved by quantitatively reproduced water equilibrium constant (p$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.
