Constructing high‐dimensional neural network potentials: A tutorial review

A lot of progress has been made in recent years in the development of atomistic potentials using machine learning (ML) techniques. In contrast to most conventional potentials, which are based on physical approximations and simplifications to derive an analytic functional relation between the atomic configuration and the potential‐energy, ML potentials rely on simple but very flexible mathematical terms without a direct physical meaning. Instead, in case of ML potentials the topology of the potential‐energy surface is “learned” by adjusting a number of parameters with the aim to reproduce a set of reference electronic structure data as accurately as possible. Due to this bias‐free construction, they are…

• DF
  Deutsche Forschungsgemeinschaft

  Awards: EXC 1069, BE3264/3-1, BE3264/5-1