Harnessing Machine Learning for Quantum-Accurate Predictions of Non-Equilibrium Behavior in 2D Materials

Abstract

Accurately predicting the non-equilibrium mechanical properties of two-dimensional (2D) materials is essential for understanding their deformation, thermo-mechanical properties, and failure mechanisms. In this study, we parameterize and evaluate two machine learning (ML) interatomic potentials, SNAP and Allegro, for modeling the non-equilibrium behavior of monolayer MoSe2. Using a density functional theory (DFT) derived dataset, we systematically compare their accuracy and transferability against the physics-based Tersoff force field. Our results show that SNAP and Allegro significantly outperform Tersoff, achieving near-DFT accuracy while maintaining computational efficiency. Allegro surpasses SNAP in both accuracy and efficiency due to its advanced neural network architecture. Both ML potentials demonstrate strong transferability, accurately predicting out-of-sample properties such as surface stability, inversion domain formation, and fracture toughness. Unlike Tersoff, SNAP and Allegro reliably model temperature-dependent edge stabilities and phase transformation pathways, aligning closely with DFT benchmarks. Notably, their fracture toughness predictions closely match experimental measurements, reinforcing their suitability for large-scale simulations of mechanical failure in 2D materials. This study establishes ML-based force fields as a powerful alternative to traditional potentials for modeling non-equilibrium mechanical properties in 2D materials.