Application of Clustering Algorithms to Partitioning Configuration Space in Fitting Reactive Potential Energy Surfaces

A large number of energy points add great difficulty to construct reactive potential energy surfaces (PES). To alleviate this, exemplar-based clustering is applied to partition the configuration space into several smaller parts. The PES of each part can be constructed easily and the global PES is ob...

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Bibliographic Details
Published in:The journal of physical chemistry. A, Molecules, spectroscopy, kinetics, environment, & general theory Vol. 122; no. 12; p. 3140
Main Authors: Guan, Yafu, Yang, Shuo, Zhang, Dong H
Format: Journal Article
Language:English
Published: United States 29.03.2018
ISSN:1520-5215, 1520-5215
Online Access:Get more information
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Summary:A large number of energy points add great difficulty to construct reactive potential energy surfaces (PES). To alleviate this, exemplar-based clustering is applied to partition the configuration space into several smaller parts. The PES of each part can be constructed easily and the global PES is obtained by connecting all of the PESs of small parts. This divide and conquer strategy is first demonstrated in the fitting of PES for OH with Gaussian process regression (GPR) and further applied to construct PESs for CH and O+CH with artificial neural networks (NN). The accuracy of PESs is tested by fitting errors and direct comparisons with previous PESs in dynamically important regions. As for OH and CH , quantum scattering calculations further validate the global accuracy of newly fitted PESs. The results suggest that partitioning the configuration space by clustering provides a simple and useful method for the construction of PESs for systems that require a large number of energy points.
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ISSN:1520-5215
1520-5215
DOI:10.1021/acs.jpca.8b00859