Fast and accurate machine-learned interatomic potentials for large-scale simulations of Cu, Al, and Ni

Machine learning (ML) has become widely used in the development of interatomic potentials for molecular dynamics simulations. However, most ML potentials are still much slower than classical interatomic potentials and are usually trained with near equilibrium simulations in mind. Here, the authors have created computationally efficient Gaussian Approximation Potentials (GAP) for large-scale simulations in Cu, Al, and Ni. The models use a selection of low-dimensional descriptors and tabulation (tabGAP), achieving orders-of-magnitude speed up compared to standard GAP. Furthermore, the models include external repulsive pair interactions, and the training databases have been designed with extra attention to far-from equilibrium simulations.