title: Representing Global Reactive Potential Energy Surfaces Using Gaussian Processes.
abstract: Representation of multidimensional global potential energy surfaces suitable for spectral and dynamical calculations from high-level ab initio calculations remains a challenge. Here, we present a detailed study on constructing potential energy surfaces using a machine learning method, namely, Gaussian process regression. Tests for the3A″ state of SH2, which facilitates the SH + H ↔ S(3P) + H2abstraction reaction and the SH + H' ↔ SH' + H exchange reaction, suggest that the Gaussian process is capable of providing a reasonable potential energy surface with a small number (∼1 × 102) of ab initio points, but it needs substantially more points (∼1 × 103) to converge reaction probabilities. The implications of these observations for construction of potential energy surfaces are discussed.
authors.affiliation: Department of Chemistry and Chemical Biology, University of New Mexico , Albuquerque, New Mexico 87131, United States.|Department of Mechanical Engineering, Massachusetts Institute of Technology , Cambridge, Massachusetts 02139, United States.
authors.forename: Brian
authors.initials: B
authors.lastname: Kolb
authors.affiliation: Department of Chemistry and Chemical Biology, University of New Mexico , Albuquerque, New Mexico 87131, United States.
authors.forename: Paul
authors.initials: P
authors.lastname: Marshall
authors.affiliation: Department of Chemistry and Chemical Biology, University of New Mexico , Albuquerque, New Mexico 87131, United States.
authors.forename: Bin
authors.initials: B
authors.lastname: Zhao
authors.affiliation: Department of Chemical Physics, University of Science and Technology of China , Hefei, Anhui 230026, China.
authors.forename: Bin
authors.identifier: http://orcid.org/0000-0003-2696-5436
authors.initials: B
authors.lastname: Jiang
authors.affiliation: Department of Chemistry and Chemical Biology, University of New Mexico , Albuquerque, New Mexico 87131, United States.
authors.forename: Hua
authors.identifier: http://orcid.org/0000-0001-9901-053X
authors.initials: H
authors.lastname: Guo