期刊
JOURNAL OF CHEMICAL PHYSICS
卷 148, 期 9, 页码 -出版社
AMER INST PHYSICS
DOI: 10.1063/1.5017103
关键词
-
资金
- European Union's Horizon 2020 research and innovation programme [646717]
- German Research Foundation (DFG) through the Cluster of Excellence in Simulation Technology at the University of Stuttgart [EXC 310/2]
- European Research Council (ERC) [646717] Funding Source: European Research Council (ERC)
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 Matern kernel and the squared exponential kernel. The Matern 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. Published by AIP Publishing.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据