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-DFT 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 DFT predictions of the shear viscosity of water are in very good agreement with experiments.

我们报道了一项针对近环境条件下液态水粘度的大规模系统性研究。该研究基于密度泛函理论（DFT），采用格林-库伯线性响应理论与平衡态从头算分子动力学（AIMD）方法开展。为满足获得可接受统计精度所需的长时模拟需求，我们通过深度神经网络势（NNP）对该从头算方法进行了增强。首先，我们以采用Perdew-Burke-Ernzerhof（PBE）交换关联泛函得到的AIMD结果为参照，对该增强方法进行验证，并对统计数据分析中关键却常被忽视的环节给予充分关注。随后，我们基于由强约束且适当归一化的SCAN-DFT泛函生成的数据集，训练了第二个NNP。当将模拟温度对标理论熔点以抵消熔化线预测偏差带来的误差后，我们基于SCAN-DFT得到的水的剪切粘度预测结果与实验数据吻合极佳。