Local Bayesian optimizer for atomic structures

Estefanía Garijo del Río, Jens Jørgen Mortensen, and Karsten Wedel Jacobsen
Phys. Rev. B 100, 104103 – Published 5 September 2019
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Abstract

A local optimization method based on Bayesian Gaussian processes is developed and applied to atomic structures. The method is applied to a variety of systems including molecules, clusters, bulk materials, and molecules at surfaces. The approach is seen to compare favorably to standard optimization algorithms like the conjugate gradient or Broyden-Fletcher-Goldfarb-Shanno in all cases. The method relies on prediction of surrogate potential energy surfaces, which are fast to optimize, and which are gradually improved as the calculation proceeds. The method includes a few hyperparameters, the optimization of which may lead to further improvements of the computational speed.

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  • Received 11 July 2019

DOI:https://doi.org/10.1103/PhysRevB.100.104103

©2019 American Physical Society

Physics Subject Headings (PhySH)

  1. Research Areas
Condensed Matter, Materials & Applied Physics

Authors & Affiliations

Estefanía Garijo del Río, Jens Jørgen Mortensen, and Karsten Wedel Jacobsen

  • CAMD, Department of Physics, Technical University of Denmark, 2800 Kongens Lyngby, Denmark

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Issue

Vol. 100, Iss. 10 — 1 September 2019

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