Viscosity in water from first-principles and deep-neural-network simulations

We report on an extensive study of the viscosity of liquid water at near-ambient conditions, performed within the Green-Kubo theory of linear response and equilibrium ab initio molecular dynamics (AIMD), based on density-functional theory (DFT). In order to cope with the long simulation times necessary to achieve an acceptable statistical accuracy, our ab initio approach is enhanced with deep-neural-network potentials (NNP). This approach is first validated against AIMD results, obtained by using the Perdew-Burke-Ernzerhof (PBE) exchange-correlation functional and paying careful attention to crucial, yet often overlooked, aspects of the statistical data analysis. Then, we train a second NNP to a dataset generated from the Strongly Constrained and Appropriately Normed (SCAN) functional. Once the error resulting from the imperfect prediction of the melting line is offset by referring the simulated temperature to the theoretical melting one, our SCAN predictions of the shear viscosity of water are in very good agreement with experiments.

本研究基于密度泛函理论（DFT），采用线性响应的格林-库博（Green-Kubo）理论与平衡态从头算分子动力学（AIMD）方法，对近常温常压条件下液态水的黏度开展了系统性研究。为满足达到可接受统计精度所需的超长模拟时长要求，我们借助深度神经网络势能面（NNP）对从头算方法进行了优化。我们首先基于采用佩尔杜-伯克-恩泽霍夫（Perdew-Burke-Ernzerhof, PBE）交换关联泛函所得到的AIMD结果，对该方法进行验证，并重点关注了统计数据分析中关键却常被忽视的细节。随后，我们采用由强约束恰当归一化（Strongly Constrained and Appropriately Normed, SCAN）泛函生成的数据集，训练了第二个深度神经网络势能面。在将模拟温度对标理论熔点以抵消熔化线预测不准带来的误差后，我们基于SCAN泛函得到的水的剪切黏度预测结果与实验值吻合极佳。