Nanosecond solvation dynamics of the hematite/liquid water interface at hybrid DFT accuracy using committee neural network potentials,Physical Chemistry Chemical Physics

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Nanosecond solvation dynamics of the hematite/liquid water interface at hybrid DFT accuracy using committee neural network potentials
Physical Chemistry Chemical Physics ( IF 2.9 ) Pub Date : 2022-06-10 , DOI: 10.1039/d2cp01708c
Philipp Schienbein ₁ , Jochen Blumberger ₁

Affiliation

Metal oxide/water interfaces play an important role in biology, catalysis, energy storage and photocatalytic water splitting. The atomistic structure at these interfaces is often difficult to characterize by experimental techniques, whilst results from ab initio molecular dynamics simulations tend to be uncertain due to the limited length and time scales accessible. In this work, we train a committee neural network potential to simulate the hematite/water interface at the hybrid DFT level of theory to reach the nanosecond timescale and systems containing more than 3000 atoms. The NNP enables us to converge dynamical properties, not possible with brute-force ab initio molecular dynamics. Our simulations uncover a rich solvation dynamics at the hematite/water interface spanning three different time scales: picosecond H-bond dynamics between surface hydroxyls and the first water layer, in-plane/out-of-plane tilt motion of surface hydroxyls on the 10 ps time scale, and diffusion of water molecules from the oxide surface characterized by a mean residence lifetime of about 60 ps. Calculation of vibrational spectra confirm that H-bonds between surface hydroxyls and first layer water molecules are stronger than H-bonds in bulk water. Our study showcases how state of the art machine learning approaches can routinely be utilized to explore the structural dynamics at transition metal oxide interfaces with complex electronic structure. It foreshadows that c-NNPs are a promising tool to tackle the sampling problem in ab initio electrochemistry with explicit solvent molecules.

中文翻译：

使用委员会神经网络电位在混合 DFT 精度下赤铁矿/液态水界面的纳秒溶剂化动力学

金属氧化物/水界面在生物学、催化、能量存储和光催化水分解中发挥着重要作用。这些界面的原子结构通常难以通过实验技术来表征，而从头算分子动力学模拟的结果由于长度和时间尺度有限而往往不确定。在这项工作中，我们训练了一个委员会神经网络潜力，以在混合 DFT 理论水平上模拟赤铁矿/水界面，以达到纳秒时间尺度和包含超过 3000 个原子的系统。NNP 使我们能够收敛动态特性，而从头开始暴力破解是不可能的分子动力学。我们的模拟揭示了跨越三个不同时间尺度的赤铁矿/水界面的丰富溶剂化动力学：表面羟基和第一水层之间的皮秒氢键动力学，表面羟基在 10 上的平面内/平面外倾斜运动ps 时间尺度，以及水分子从氧化物表面的扩散，其特征是平均停留寿命约为 60 ps。振动光谱的计算证实表面羟基和第一层水分子之间的氢键比散装水中的氢键强。我们的研究展示了如何经常利用最先进的机器学习方法来探索具有复杂电子结构的过渡金属氧化物界面的结构动力学。它预示着c-NNPs是解决抽样问题的有前途的工具具有显式溶剂分子的从头算电化学。

更新日期：2022-06-15

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