Accurate prediction of heat conductivity of water by a neuroevolution potential

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.

8 pages, 7 figures

References in corpus (4)

• Canonical sampling through velocity-rescaling
• Pressure control using stochastic cell rescaling
• How van der Waals interactions determine the unique properties of water
• Transferability of neural network potentials for varying stoichiometry: phonons and thermal conductivity of Mn${}_x$Ge${}_y$ compounds

Cited by in corpus (1)

• Mechanisms of temperature-dependent thermal transport in amorphous silica from machine-learning molecular dynamics