Generalization of SNAP to arbitrary machine-learning interatomic potentials in LAMMPS

SNAP is an automated methodology for generating accurate and robust application-specific machine-learning interatomic potentials (MLIAPs) in LAMMPS. The MLIAP package generalizes SNAP to arbitrary MLIAPs. This is accomplished by separating the energy model (e.g. linear, non-linear, Gaussian process, neural network) from the local atomic neighborhood descriptors (e.g. ACE, Behler-Parrinello, DeepPot, SNAP, SOAP). Any new model added to the MLIAP package can be combined with any existing descriptor to compute energy and forces, and vice versa. Gradients of energy and forces w.r.t. model parameters can also be computed for training MLIAPs against ab initio data. I will discuss the underlying algorithms and describe some interesting applications.