Construction of reactive potential energy surfaces with Gaussian process regression: active data selection

Gaussian process regression (GPR) is an efficient non-parametric method for constructing multi-dimensional potential energy surfaces (PESs) for polyatomic molecules. Since not only the posterior mean but also the posterior variance can be easily calculated, GPR provides a well-established model for...

Full description

Saved in:
Bibliographic Details
Published inMolecular physics Vol. 116; no. 7-8; pp. 823 - 834
Main Authors Guan, Yafu, Yang, Shuo, Zhang, Dong H.
Format Journal Article
LanguageEnglish
Published Abingdon Taylor & Francis Ltd 18.04.2018
Subjects
Construction methods
Gaussian process
Polyatomic molecules
Potential energy
Online AccessGet full text

Cover

More Information
Summary:Gaussian process regression (GPR) is an efficient non-parametric method for constructing multi-dimensional potential energy surfaces (PESs) for polyatomic molecules. Since not only the posterior mean but also the posterior variance can be easily calculated, GPR provides a well-established model for active learning, through which PESs can be constructed more efficiently and accurately. We propose a strategy of active data selection for the construction of PESs with emphasis on low energy regions. Through three-dimensional (3D) example of H3, the validity of this strategy is verified. The PESs for two prototypically reactive systems, namely, H + H2O [left right arrow] H2 + OH reaction and H + CH4 [left right arrow] H2 + CH3 reaction are reconstructed. Only 920 and 4000 points are assembled to reconstruct these two PESs respectively. The accuracy of the GP PESs is not only tested by energy errors but also validated by quantum scattering calculations.
ISSN:0026-8976
1362-3028
DOI:10.1080/00268976.2017.1407460