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.
@article{arxiv.2302.12328,
title = {Accurate prediction of heat conductivity of water by a neuroevolution potential},
author = {Ke Xu and Yongchao Hao and Ting Liang and Penghua Ying and Jianbin Xu and Jianyang Wu and Zheyong Fan},
journal= {arXiv preprint arXiv:2302.12328},
year = {2023}
}