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Machine-learned interatomic potential for predictive simulation of MoS2 epitaxy

Emir Bilgili1,*, Nicholas Taormina1, Richard Hennig2, Simon R. Phillpot2, and Youping Chen1

  • 1Department of Mechanical and Aerospace Engineering, University of Florida, Gainesville, Florida 32611, USA
  • 2Department of Materials Science and Engineering, University of Florida, Gainesville, Florida 32611, USA

  • *Contact author: emir.bilgili@ufl.edu
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Phys. Rev. Materials 10, 054002 – Published 19 May, 2026

DOI: https://doi.org/10.1103/71xp-pjb6

Abstract

A machine-learned interatomic potential (MLIP) for multilayer MoS2 was developed using the ultrafast 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 (R² = 0.91) across ten defective monolayers and reproducing the difference between the free energies of zigzag and armchair edges within 5% of DFT. Nonequilibrium 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 approximately two times slower than the fastest empirical potentials, enables large-scale atomistic simulations of MoS2 epitaxial growth.

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References (92)

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Phys. Rev. Materials 10, 054002– Published 19 May, 2026
  • Received 18 December 2025
  • Accepted 24 April 2026
Article part of CHORUS
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