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Gaussian process regression for geometry optimization.
- Source :
-
Journal of Chemical Physics . 2018, Vol. 148 Issue 9, p1-1. 1p. 2 Diagrams, 1 Chart, 6 Graphs. - Publication Year :
- 2018
-
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 Limited-memory Broyden–Fletcher–Goldfarb–Shanno optimizer of the DL-FIND library on 26 test systems, we found the new optimizer to generally reduce the number of required optimization steps. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 00219606
- Volume :
- 148
- Issue :
- 9
- Database :
- Academic Search Index
- Journal :
- Journal of Chemical Physics
- Publication Type :
- Academic Journal
- Accession number :
- 128367281
- Full Text :
- https://doi.org/10.1063/1.5017103