Progress Towards a Quantum-Accurate Classical SNAP Machine Learning Interaction Potential for Gold

Classical molecular dynamics is a powerful method of describing the properties of a material, but is often inconsistent with quantum molecular dynamics. Quantum molecular dynamics, while more accurate, is more computationally intensive and not feasible for systems larger than 1000 atoms. We use machine learning to connect quantum accuracy and scalability of classical interatomic potentials. We report on progress made testing the performance of a quantum-accurate classical interatomic potential for gold against state-of-the-art classical potentials in describing experimentally verified properties of gold using LAMMPS (Large Scale Atomic/Molecular Massively Parallel Simulator) Preliminary results show close agreement with quantum molecular dynamics simulations, while being computationally less expensive.