Representing Global Reactive Potential Energy Surfaces Using Gaussian Processes

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This article presents a detailed study on constructing potential energy surfaces using a machine learning method, namely, Gaussian process regression.

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6 p.

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Kolb, Brian; Marshall, Paul; Zhao, Bin; Jiang, Bin & Guo, Hua March 13, 2017.

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This article is part of the collection entitled: UNT Scholarly Works and was provided by UNT College of Arts and Sciences to Digital Library, a digital repository hosted by the UNT Libraries. It has been viewed 29 times , with 6 in the last month . More information about this article can be viewed below.

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  • Kolb, Brian University of New Mexico; Massachusetts Institute of Technology
  • Marshall, Paul University of New Mexico; University of North Texas
  • Zhao, Bin University of New Mexico; Universität Bielefeld
  • Jiang, Bin University of Science and Technology of China
  • Guo, Hua University of New Mexico

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Description

This article presents a detailed study on constructing potential energy surfaces using a machine learning method, namely, Gaussian process regression.

Physical Description

6 p.

Notes

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 the 3A″ state of SH2, which facilitates the SH + H ↔ S(3P) + H2 abstraction 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.

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  • Journal of Physical Chemistry A, 2017. Washington, D.C.: American Chemical Society

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Publication Information

  • Publication Title: Journal of Physical Chemistry A
  • Volume: 121
  • Pages: 2552-2557
  • Peer Reviewed: Yes

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UNT Scholarly Works

Materials from the UNT community's research, creative, and scholarly activities and UNT's Open Access Repository. Access to some items in this collection may be restricted.

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Submitted Date

  • February 6, 2017

Accepted Date

  • March 12, 2017

Creation Date

  • March 13, 2017

Added to The UNT Digital Library

  • Aug. 29, 2017, 9:38 a.m.

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Total Uses: 29

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Kolb, Brian; Marshall, Paul; Zhao, Bin; Jiang, Bin & Guo, Hua. Representing Global Reactive Potential Energy Surfaces Using Gaussian Processes, article, March 13, 2017; Washington, DC. (digital.library.unt.edu/ark:/67531/metadc990976/: accessed October 23, 2018), University of North Texas Libraries, Digital Library, digital.library.unt.edu; crediting UNT College of Arts and Sciences.