Minimum energy path calculations with Gaussian process regression
https://doi.org/10.17586/2220-8054-2016-7-6-925-935
Abstract
The calculation of minimum energy paths for transitions such as atomic and/or spin rearrangements is an important task in many contexts and can often be used to determine the mechanism and rate of transitions. An important challenge is to reduce the computational effort in such calculations, especially when ab initio or electron density functional calculations are used to evaluate the energy since they can require large computational effort. Gaussian process regression is used here to reduce significantly the number of energy evaluations needed to find minimum energy paths of atomic rearrangements. By using results of previous calculations to construct an approximate energy surface and then converge to the minimum energy path on that surface in each Gaussian process iteration, the number of energy evaluations is reduced significantly as compared with regular nudged elastic band calculations. For a test problem involving rearrangements of a heptamer island on a crystal surface, the number of energy evaluations is reduced to less than a fifth. The scaling of the computational effort with the number of degrees of freedom as well as various possible further improvements to this approach are discussed.
Keywords
Supporting agencies: HJ would like to thank Prof. Andrew Peterson at Brown University for helpful discussions. This work was supported by the Academy of Finland (FiDiPro program grant no. 263294) and by the Icelandic Research Fund.
About the Authors
O-P. Koistinen
Helsinki Institute for Information Technology HIIT; Aalto University
Finland
Finland
Department of Computer Science
E. Maras
Aalto University
Finland
Finland
Department of Applied Physics
H. Jonsson
University of Iceland
Iceland
Iceland
Faculty of Physical Sciences
107 Reykjav´ık
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Review
For citations:
Koistinen O., Maras E., Jonsson H. Minimum energy path calculations with Gaussian process regression. Nanosystems: Physics, Chemistry, Mathematics. 2016;7(6):925-935. https://doi.org/10.17586/2220-8054-2016-7-6-925-935
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