Abstract: Molybdenum carbide has various applications for which studying the material using classical molecular dynamics simulations would be valuable. Here, we develop an empirical potential within the Tersoff formalism using particle swarm optimization for the orthorhombic phase of Mo2C. The developed potential is shown to predict lattice constants, elastic properties, and equation of state results that are consistent with current and previously reported results from experiments and first principles calculations. We demonstrate the potential with simulations of indentation using multiple indenter sizes that load and unload in three different directions relative to the crystallographic lattice of orthorhombic Mo2C. Direction-dependent force-displacement trends are analyzed and explained in terms of the spatial distributions of stress and strain within the material during indentation. This study reveals the anisotropic elasticity of orthorhombic Mo2C and, more generally, provides researchers with a new empirical potential that can be used to explore the properties and behavior of the material going forward.

Abstract: Direct experimental measurement of thermal expansion coefficient without substrate effects is a challenging task for two-dimensional (2D) materials, and its accurate estimation with large-scale ab initio molecular dynamics is computationally very expensive. Machine learning-based interatomic potentials trained with ab initio data have been successfully used in molecular dynamics simulations to decrease the computational cost without compromising the accuracy. In this study, we investigated using Gaussian approximation potentials to reproduce the density functional theory-level accuracy for graphene within both lattice dynamical and molecular dynamical methods, and to extend their applicability to larger length and time scales. Two such potentials are considered, GAP17 and GAP20. GAP17, which was trained with pristine graphene structures, is found to give closer results to density functional theory calculations at different scales. Further vibrational and structural analyses verify that the same conclusions can be deduced with density functional theory level in terms of the reasoning of the thermal expansion behavior, and the negative thermal expansion behavior is associated with long-range out-of-plane phonon vibrations. Thus, it is argued that the enabled larger system sizes by machine learning potentials may even enhance the accuracy compared to small-size-limited ab initio molecular dynamics.

Abstract: We present here a new interatomic potential parameter set to predict the thermal conductivity of zirconium trisulfide monolayers. The generated Tersoff-type force field is parameterized using data collected with first-principles calculations. We use non-equilibrium molecular dynamics simulations to predict the thermal conductivity. The generated parameters result in very good agreement in structural, mechanical, and dynamical parameters. The room temperature lattice thermal conductivity ( kappa) of the considered crystal is predicted to be kappa x x = 25.69Wm – 1K – 1 and kappa y y = 42.38Wm – 1K – 1, which both agree well with their corresponding first-principles values with a discrepancy of less than 5%. Moreover, the calculated kappa variation with temperature (200 and 400 K) are comparable within the framework of the accuracy of both first-principles and molecular dynamics simulations.