Potential energy surfaces fitted by artificial neural networks.

Abstract

Molecular mechanics is the tool of choice for the modeling of systems that are so large or complex that it is impractical or impossible to model them by ab initio methods. For this reason there is a need for accurate potentials that are able to quickly reproduce ab initio quality results at the fraction of the cost. The interactions within force fields are represented by a number of functions. Some interactions are well understood and can be represented by simple mathematical functions while others are not so well understood and their functional form is represented in a simplistic manner or not even known. In the last 20 years there have been the first examples of a new design ethic, where novel and contemporary methods using machine learning, in particular, artificial neural networks, have been used to find the nature of the underlying functions of a force field. Here we appraise what has been achieved over this time and what requires further improvements, while offering some insight and guidance for the development of future force fields.

DOI: 10.1021/jp9105585

Cite this paper

@article{Handley2010PotentialES, title={Potential energy surfaces fitted by artificial neural networks.}, author={Chris Handley and Paul L. A. Popelier}, journal={The journal of physical chemistry. A}, year={2010}, volume={114 10}, pages={3371-83} }