Comprehending water dynamics is crucial in various fields, such as water desalination, ion separation, electrocatalysis, and biochemical processes. While ab initio molecular dynamics (AIMD) accurately portray water's structure, computing its dynamic properties over nanosecond time scales proves cost-prohibitive. This study employs machine learning potentials (MLPs) to accurately determine the dynamic properties of liquid water with ab initio accuracy. Our findings reveal diversity in the calculated diffusion coefficient (D) and viscosity of water (η) across different methodologies. Specifically, while the GGA, meta-GGA, and hybrid functional methods struggle to predict dynamic properties under ambient conditions, methods on the higher level of Jacob's ladder of DFT approximation perform significantly better. Intriguingly, we discovered that both D and η adhere to the established Stokes-Einstein (SE) relation for all of the ab initio water. The diversity observed across different methods can be attributed to distinct structural entropy, affirming the applicability of excess entropy scaling relations across all functionals. The correlation between D and η provides valuable insights for identifying the ideal temperature to accurately replicate the dynamic properties of liquid water. Furthermore, our findings can validate the rationale behind employing artificially high temperatures in the simulation of water via AIMD. These outcomes not only pave the path to designing better functionals for water but also underscore the significance of water's many-body characteristics.

中文翻译：

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Ab Initio Water 的动态多样性和不变性。


了解水动力学在各个领域都至关重要，例如海水淡化、离子分离、电催化和生化过程。虽然从头计算分子动力学 （AIMD） 准确地描绘了水的结构，但在纳秒时间尺度上计算其动态特性被证明成本高昂。本研究采用机器学习电位 （MLP） 以 ab initio 精度准确确定液态水的动态特性。我们的研究结果揭示了不同方法中计算出的扩散系数 （D） 和水的粘度 （η） 的多样性。具体来说，虽然 GGA、meta-GGA 和混合泛函方法难以预测环境条件下的动态特性，但 DFT 近似的 Jacob 阶梯较高级别的方法性能明显更好。有趣的是，我们发现 D 和 η 都遵循所有从头计算的水的既定斯托克斯-爱因斯坦 （SE） 关系。在不同方法中观察到的多样性可归因于不同的结构熵，这证实了超额熵缩放关系在所有泛函中的适用性。D 和 η 之间的相关性为确定理想温度以准确复制液态水的动态特性提供了有价值的见解。此外，我们的研究结果可以验证通过 AIMD 模拟水时采用人工高温的基本原理。这些结果不仅为设计更好的水泛函铺平了道路，而且还强调了水的多体特性的重要性。