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.
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