An overview of computational methods to describe high-dimensional potential energy surfaces suitable for atomistic simulations is given. Particular emphasis is put on accuracy, computability, transferability and extensibility of the methods discussed. They include empirical force fields, representations based on reproducing kernels, using permutationally invariant polynomials, and neural network-learned representations and combinations thereof. Future directions and potential improvements are discussed primarily from a practical, application-oriented perspective.
@article{arxiv.1910.03044,
title = {High-Dimensional Potential Energy Surfaces for Molecular Simulations},
author = {Oliver T. Unke and Debasish Koner and Sarbani Patra and Silvan Käser and Markus Meuwly},
journal= {arXiv preprint arXiv:1910.03044},
year = {2020}
}