Machine Learning in Computational Chemistry: An Evaluation of Method Performance for Nudged Elastic Band Calculations

摘要

The localization of transition states and the calculation of reaction pathways are routine tasks of computational chemists but often very CPU-intense problems, in particular for large systems. The standard algorithm for this purpose is the nudged elastic band method, but it has become obvious that an “intelligent” selection of points to be evaluated on the potential energy surface can improve its convergence significantly. This article summarizes, compares, and extends known strategies that have been heavily inspired by the machine learning developments of recent years. It presents advantages and disadvantages and provides an unbiased comparison of neural network based approaches, Gaussian process regression in Cartesian coordinates, and Gaussian approximation potentials. We test their performance on two example reactions, the ethane rotation and the activation of carbon dioxide on a metal catalyst, and provide a clear ranking in terms of usability for future implementations.