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Gaussian Process Regression for Geometry Optimization
- Source :
- J. Chem. Phys. 148, 094114 (2018)
- Publication Year :
- 2020
-
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.
- Subjects :
- Physics - Chemical Physics
Subjects
Details
- Database :
- arXiv
- Journal :
- J. Chem. Phys. 148, 094114 (2018)
- Publication Type :
- Report
- Accession number :
- edsarx.2009.05803
- Document Type :
- Working Paper
- Full Text :
- https://doi.org/10.1063/1.5017103