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arXiv · 2302.12328

Accurate prediction of heat conductivity of water by a neuroevolution potential

Ke Xu·Yongchao Hao·Ting Liang·Penghua Ying·Jianbin Xu·Jianyang Wu·Zheyong Fan
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Abstract

We propose an approach that can accurately predict the heat conductivity of liquid water. On the one hand, we develop an accurate machine-learned potential based on the neuroevolution-potential approach that can achieve quantum-mechanical accuracy at the cost of empirical force fields. On the other hand, we combine the Green-Kubo method and the spectral decomposition method within the homogeneous nonequilibrium molecular dynamics framework to account for the quantum-statistical effects of high-frequency vibrations. Excellent agreement with experiments under both isobaric and isochoric conditions within a wide range of temperatures is achieved using our approach.

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Ke Xu, Yongchao Hao, Ting Liang, Penghua Ying, Jianbin Xu, Jianyang Wu, Zheyong Fan. 2023-02-18. Accurate prediction of heat conductivity of water by a neuroevolution potential. https://doi.org/10.1063/5.0147039

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