Issue 9, 2023

Thermal transport across copper–water interfaces according to deep potential molecular dynamics

Abstract

Nanoscale thermal transport at solid–liquid interfaces plays an essential role in many engineering fields. This work performs deep potential molecular dynamics (DPMD) simulations to investigate thermal transport across copper–water interfaces. Unlike traditional classical molecular dynamics (CMD) simulations, we independently train a deep learning potential (DLP) based on density functional theory (DFT) calculations and demonstrated its high computational efficiency and accuracy. The trained DLP predicts radial distribution functions (RDFs), vibrational densities of states (VDOS), density curves, and thermal conductivity of water confined in the nanochannel at a DFT accuracy. The thermal conductivity decreases slightly with an increase in the channel height, while the influence of the cross-sectional area is negligible. Moreover, the predicted interfacial thermal conductance (ITC) across the copper–water interface by DPMD is 2.505 × 108 W m−2 K−1, the same order of magnitude as the CMD and experimental results but with a high computational accuracy. This work seeks to simulate the thermal transport properties of solid–liquid interfaces with DFT accuracy at large-system and long-time scales.

Graphical abstract: Thermal transport across copper–water interfaces according to deep potential molecular dynamics

Supplementary files

Article information

Article type
Paper
Submitted
27 noy 2022
Accepted
29 yan 2023
First published
20 fev 2023

Phys. Chem. Chem. Phys., 2023,25, 6746-6756

Thermal transport across copper–water interfaces according to deep potential molecular dynamics

Z. Li, X. Tan, Z. Fu, L. Liu and J. Yang, Phys. Chem. Chem. Phys., 2023, 25, 6746 DOI: 10.1039/D2CP05530A

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