Gaussian Process Regression for Geometry Optimization

Alexander Denzel; Johannes Kästner

doi:10.1063/1.5017103

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arXiv · 2009.05803

Gaussian Process Regression for Geometry Optimization

Alexander Denzel·Johannes Kästner

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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 Matérn kernel and the squared exponential kernel. The Matérn 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.

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Alexander Denzel, Johannes Kästner. 2020-09-12. Gaussian Process Regression for Geometry Optimization. https://doi.org/10.1063/1.5017103

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