Machine-learned interatomic potential for predictive simulation of <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>Mo</mml:mi> <mml:msub> <mml:mi mathvariant="normal">S</mml:mi> <mml:mn>2</mml:mn> </mml:msub> </mml:mrow>

A machine-learned interatomic potential (MLIP) for multilayer $\mathrm{Mo}{\mathrm{S}}_{2}$ 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) $\mathrm{Mo}{\mathrm{S}}_{2}$ phases. Critically, defect and edge formation energies are captured with high fidelity, exhibiting a strong correlation with DFT (R\texttwosuperior{} = 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 $\mathrm{Mo}{\mathrm{S}}_{2}$ epitaxial growth.