Gaussian Process Regression for Geometry Optimization
Chemical Physics
2020-09-15 v1
Abstract
We implemented a geometry optimizer based on Gaussian process regression (GPR) to find minimum structures on potential energy surfaces. We tested both a two times differentiable form of the Mat\'{e}rn kernel and the squared exponential kernel. The Mat\'{e}rn kernel performs much better. We give a detailed description of the optimization procedures. These include overshooting the step resulting from GPR in order to obtain a higher degree of interpolation vs. extrapolation. In a benchmark against the L-BFGS optimizer of the DL-FIND library on 26 test systems, we found the new optimizer to generally reduce the number of required optimization steps.
Cite
@article{arxiv.2009.05803,
title = {Gaussian Process Regression for Geometry Optimization},
author = {Alexander Denzel and Johannes Kästner},
journal= {arXiv preprint arXiv:2009.05803},
year = {2020}
}