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.

我们报告了基于密度泛函理论 (DFT) 在线性响应和平衡从头算分子动力学 (AIMD) 的 Green-Kubo 理论中进行的近环境条件下液态水粘度的广泛研究。为了应对实现可接受的统计精度所需的较长模拟时间，我们的从头算方法用深度神经网络电位（NNP）增强。该方法首先针对 AIMD 结果进行了验证，该结果是通过使用 Perdew-Burke-Ernzerhof (PBE) 交换相关函数并密切关注统计数据分析的关键但经常被忽视的方面获得的。然后，我们将第二个 NNP 训练为从强约束和适当规范 (SCAN) 函数生成的数据集。一旦通过将模拟温度参考理论熔化温度来抵消因熔化线预测不完善而导致的误差，我们对水的剪切粘度的 SCAN 预测与实验非常吻合。