High-Dimensional Neural Network Potentials for Atomistic Simulations

Machine-learning methods have become increasingly popular for describing potential energy surfaces for molecular and materials simulations, and they are even beginning to challenge the present-day dominance of force fields for this task. This chapter reviews high-dimensional neural network potentials (HDNNPs), which are a general-purpose reactive potential method that can be used for simulations of an arbitrary number of atoms, can describe all types of chemical interactions (e.g., covalent, metallic, and dispersion), and includes the breaking and forming of chemical bonds. Before an HDNNP can be applied, it must be parameterized using electronic structure data, and great care must be taken at the parameterization stage to ensure that all pertinent parts of the potential energy surface are adequately covered. Typically, this is done iteratively through the addition of more training data and refitting of parameters. This chapter illustrates these points through the use of two case studies from our recent work for aqueous NaOH solutions and the ZnO/water interface.