Machine-Learned Interatomic Potential for Predictive Simulation of MoS2 Epitaxy

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.

The UF3 potential file, input scripts for epitaxial growth simulations, and the Supplemental Material are available at https://github.com/uf-chenlab/UF3-MoS2. The UF3Tools code can be found at: https://github.com/uf-chenlab/UF3Tools

References in corpus (10)

• VASPKIT: A User-friendly Interface Facilitating High-throughput Computing and Analysis Using VASP Code
• Implementation strategies in phonopy and phono3py
• van der Waals forces in density functional theory: The vdW-DF method
• Molecular Dynamics Simulations of Single-Layer Molybdenum Disulphide (MoS2): Stillinger-Weber Parametrization, Mechanical Properties, and Thermal Conductivity
• Parametrization of Stillinger-Weber Potential Based on Valence Force Field Model: Application to Single-Layer MoS2 and Black Phosphorus
• Elastic properties of bulk and low-dimensional materials using Van der Waals density functional
• Ultra-fast interpretable machine-learning potentials
• Combining phonon accuracy with high transferability in Gaussian approximation potential models
• Molecular potentials for 2D molybdenum disulphide: transferability and performance
• Benchmarking CHGNet Universal Machine Learning Interatomic Potential Against DFT and EXAFS: Case of Layered WS2 and MoS2