Abstract

Neural network potentials (NNPs) have demonstrated the effectiveness of machine-learning tools in the context of atomistic simulations. This approach, which is based on artificial neural networks trained to accurately reproduce ab initio potential energy surfaces, offers two major advantages. First, with respect to the underlying reference method the computational effort to calculate energies and forces is drastically reduced, which allows to sample large system sizes and long time scales in molecular dynamics simulations. In addition, unlike empirical potentials, the neural network at the very heart of the method is not limited by an approximate functional form but can flexibly adjust to the reference potential energy surface.

In this MLDG tutorial session I will briefly introduce the method, continue with a presentation of the software package n2p2 and provide also real-world examples of NNP training and application.