利用机器学习力场从局部结构到水的热力学与输运
From Local Structure to Thermodynamics and Transport of Water with Machine Learning Force Fields
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中文总结 AI 辅助
研究利用机器学习力场评估液态水相关性质，不同密度泛函理论交换关联泛函会影响预测结果；RPBE-D3与实验结果最一致，经典SPC/E模型与RPBE-D3有相似性，揭示了平动和取向熵贡献与自扩散系数的关系。
中文摘要 AI 辅助
我们使用液态水的全六维对关联函数、三体结构描述符、过量熵和输运性质，评估了源自不同密度泛函理论交换关联泛函的机器学习力场。预测的微观结构和动力学强烈依赖于基础泛函：忽略色散会产生明显的过度结构化、过度负的过量熵和受抑制的扩散。平动和取向熵贡献紧密耦合，共同与约化自扩散系数呈现明确关系。在测试模型中，RPBE-D3在结构、热力学和输运性质方面与实验结果最为一致。经典的SPC/E模型作为额外参考，与RPBE-D3有显著相似性，这与两个模型可比的玻恩有效电荷和部分电荷一致。
英文摘要
We evaluate machine learning force fields derived from different density functional theory exchange correlation functionals using the full six-dimensional pair correlation function of liquid water, three-body structural descriptors, excess entropy, and transport properties. The predicted microscopic structure and dynamics depend strongly on the underlying functional: neglecting dispersion produces pronounced overstructuring, overly negative excess entropy, and suppressed diffusion. Translational and orientational entropy contributions are tightly coupled and together exhibit a clear relationship with the reduced selfdiffusion coefficient. Among the tested models, RPBE-D3 provides the most consistent agreement with experiment across structural, thermodynamic, and transport properties. The classical SPC/E model serves as an additional reference and displays notable similarities to RPBE-D3, consistent with the comparable Born effective and partial charges of the two models.
