Machine-learned Interatomic Potential for Ti$_{n+1}$C$_n$ MXenes: Application to Ion Irradiation Simulations

TL;DR

This paper develops a machine-learned interatomic potential for Ti$_{n+1}$C$_n$ MXenes, enabling accurate and efficient molecular dynamics simulations of ion irradiation effects for defect engineering and material design.

Contribution

It introduces a novel ML potential for Ti$_{n+1}$C$_n$ MXenes, validated across diverse structures, facilitating advanced atomistic simulations of irradiation processes.

Findings

ML potential accurately predicts bond environments in MXenes

Simulations reveal ion irradiation effects like sputtering and defect formation

Guidelines for defect engineering via ion irradiation are established

Abstract

A computationally efficient and accurate machine-learned (ML) interatomic potential is developed for Ti$_{n+1}$C$_n$ MXenes. With a diverse set of structures computed with density functional theory, the trained ML potential demonstrates good accuracy and robustness to a wide range of bond distances and environments, making it a useful tool for molecular dynamics simulations of MXenes subjected to mechanical load or irradiation. The ML potential is applied to simulations of light and heavy ion irradiation, gathering insight into the statistics and probabilities of sputtering, reflection, defect creation, and implantation into Ti$_{n+1}$C$_n$ MXene sheets. The results provide guidelines for defect engineering of MXenes through ion irradiation and implantation. Additionally, the ML potential development provides a landmark recipe for enabling machine-learning-driven atomistic simulations of other MXenes.