Restricted-Variance Constrained, Reaction Path, and Transition State Molecular Optimizations Using Gradient-Enhanced Kriging

14 December 2020, Version 2
This content is a preprint and has not undergone peer review at the time of posting.

Abstract

Gaussian process regression has recently been explored as an alternative to standard surrogate models in molecular equilibrium geometry optimization. In particular, the gradient-enhanced Kriging approach in association with internal coordinates, restricted-variance optimization, and an efficient and fast estimate of hyperparameters have demonstrated performance on par or better than standard methods. In this report we extend the approach to constrained optimizations and transition states, and benchmark it for a set of reactions. We compare the performance of the new developed method with the standard techniques in the location of transition states and in constrained optimizations, both isolated and in the context of reaction path computation. The results show that the method outperforms the current standard in efficiency as well as in robustness.

Keywords

Geometry optimization
Constrained optimization
Transition state optimization
Reaction path optimization
Gaussian process regression
Kriging

Supplementary materials

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