Machine-Learned Interatomic Potential for Predictive Simulation of MoS2 Epitaxy

TL;DR

This paper presents a machine-learned interatomic potential for multilayer MoS2 that accurately reproduces key properties and enables large-scale simulations of epitaxial growth, closely matching DFT results.

Contribution

The development of a fast, accurate MLIP for MoS2 that captures structural, energetic, and dynamic properties, facilitating large-scale predictive simulations.

Findings

Reproduces lattice constants, binding energies, and phonon spectra with high accuracy.

Captures defect and edge formation energies with R^2 = 0.91 correlation to DFT.

Simulates epitaxial growth consistent with experimental observations.

Abstract

A machine-learned interatomic potential (MLIP) for multilayer MoS2 was developed using the ultra-fast force field (UF3) framework. The UF3 MLIP reproduces key properties in strong agreement with DFT including lattice constants, interlayer binding energies, and phase-stability. Furthermore, the potential reasonably captures the phonon spectra and the highly anisotropic elastic tensor across monolayer (1H) and bulk (2H, 3R) MoS2 phases. Critically, defect and edge formation energies are captured with high fidelity, exhibiting a strong correlation with DFT (R^2 = 0.91) across ten defective monolayers and reproducing the relative difference between the free energies of zigzag and armchair edges within 5% of DFT. Non-equilibrium molecular dynamics simulations reveal layered homoepitaxial growth consistent with experimental observations, demonstrating the formation of van der Waals gaps between successive epilayers and triangular domains bounded by zigzag edges. The robust UF3 MLIP, which is only ~2X slower than the fastest empirical potentials, enables large-scale atomistic simulations of MoS2 epitaxial growth.