High-Dimensional Potential Energy Surfaces for Molecular Simulations

TL;DR

This paper reviews various computational methods for modeling high-dimensional potential energy surfaces in molecular simulations, focusing on their accuracy, efficiency, transferability, and future improvements.

Contribution

It provides a comprehensive overview of existing methods including empirical, kernel-based, polynomial, and neural network approaches, highlighting their strengths and limitations.

Findings

Neural network methods offer promising accuracy and flexibility.

Transferability remains a key challenge across methods.

Future research should focus on improving computational efficiency and transferability.

Abstract

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.