Machine Learning Interatomic Potentials Enable Molecular Dynamics Simulations of Doped MoS2

TL;DR

This work provides an open-source computational workflow for application-oriented design of doped-MoS2, enabling high-throughput screening of dopant candidates and optimization of compositions for targeted tribological, electronic, and optoelectronic performance.

Abstract

We present the first computational framework for molecular dynamics simulation of MoS2 doped with 25 elements spanning metals, non-metals, and transition metals using Meta's Universal Model for Atoms machine learning interatomic potential (MLIP). Benchmarking against density functional theory calculations demonstrates the accuracy of the MLIP for simulating doped-MoS2 systems and highlights opportunities for improvement. Using the MLIP, we perform heating-cooling simulations of doped-MoS2 supercells. The simulations capture complex phenomena including dopant clustering, MoS2 layer fracturing, interlayer diffusion, and chemical compound formation at orders-of-magnitude reduced computational cost compared to density functional theory. This work provides an open-source computational workflow for application-oriented design of doped-MoS2, enabling high-throughput screening of dopant candidates and optimization of compositions for targeted tribological, electronic, and optoelectronic performance. The MLIP bridges the accuracy-efficiency gap between first-principles methods and empirical potentials, and the framework offers unprecedented opportunities for large-scale materials discovery in two-dimensional doped material systems.