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（density-functional theory, DFT），在线性响应的格林-库博（Green-Kubo）理论框架下结合平衡态从头算分子动力学（ab initio molecular dynamics, AIMD）方法，对近环境条件下液态水的黏度开展了大规模系统性研究。为解决达成可接受统计精度所需的长模拟时长难题，我们通过深度神经网络势（deep-neural-network potentials, NNP）对从头算方法进行了增强优化。本研究首先以采用珀德-伯克-恩泽霍夫（Perdew-Burke-Ernzerhof, PBE）交换关联泛函所获得的AIMD模拟结果作为参照，对所提方法进行验证，同时重点关注统计数据分析中至关重要却常被忽视的细节环节。随后，我们针对由强约束适度归一化（Strongly Constrained and Appropriately Normed, SCAN）泛函生成的数据集，训练了第二套深度神经网络势。当通过将模拟温度对标理论熔点以修正熔化线预测不准所引入的误差后，我们基于SCAN泛函得到的水剪切黏度预测结果与实验数据吻合极佳。