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.
- This article is part of the themed collection: Insightful Machine Learning for Physical Chemistry
Please wait while we load your content...
