Boosting current-induced molecular dynamics with machine-learning potential

TL;DR

This paper introduces a machine-learning potential to significantly accelerate current-induced molecular dynamics simulations, enabling detailed studies of energy transfer processes in single-molecular junctions with improved computational efficiency.

Contribution

The authors develop a machine-learning potential that achieves DFT-level accuracy, vastly speeding up molecular dynamics simulations of SMJs under current, and demonstrate its application in comparing heat transport in graphene versus gold electrodes.

Findings

Graphene electrodes produce an order of magnitude less heating than gold electrodes.

Machine-learning potentials enable efficient and accurate MD simulations of SMJs.

Graphene's superior phonon spectral overlap enhances heat transport in SMJs.

Abstract

In a current-carrying single-molecular junction (SMJ), a hierarchy of hybrid energy transport processes takes place under a highly nonequilibrium situation, including energy transfer from electrons to molecular vibrations via electron-vibration interaction, energy redistribution within different vibrational modes via anharmonic coupling, and eventual energy transport to surrounding electrodes. A comprehensive understanding of such processes is a prerequisite for their potential applications as single-molecular devices. $Ab$ $initio$ current-induced molecular dynamics (MD) is an ideal approach to address this complicated problem. But the computational cost hinders its usage in systematic study of realistic SMJs. Here, we achieve orders of magnitude improvement in the speed of MD simulation by employing machine-learning potential with accuracy comparable to density functional theory. Using this approach, we show that SMJs with graphene electrodes generate order of magnitude less heating than those with gold electrodes. Our work illustrates the superior heat transport property of graphene as electrodes for SMJs, thanks to its better phonon spectral overlap with molecular vibrations.