Bauer, Maximilian, Probert, Matt orcid.org/0000-0002-1130-9316 and Panosetti, Chiara (2022) Systematic Comparison of Genetic Algorithm and Basin Hopping Approaches to the Global Optimization of Si(111) Surface Reconstructions. Journal of Physical Chemistry A. pp. 3043-3056. ISSN 1089-5639
Abstract
We present a systematic study of two widely used material structure prediction methods, the Genetic Algorithm and Basin Hopping approaches to global optimization, in a search for the 3 × 3, 5 × 5, and 7 × 7 reconstructions of the Si(111) surface. The Si(111) 7 × 7 reconstruction is the largest and most complex surface reconstruction known, and finding it is a very exacting test for global optimization methods. In this paper, we introduce a modification to previous Genetic Algorithm work on structure search for periodic systems, to allow the efficient search for surface reconstructions, and present a rigorous study of the effect of the different parameters of the algorithm. We also perform a detailed comparison with the recently improved Basin Hopping algorithm using Delocalized Internal Coordinates. Both algorithms succeeded in either resolving the 3 × 3, 5 × 5, and 7 × 7 DAS surface reconstructions or getting “sufficiently close”, i.e., identifying structures that only differ for the positions of a few atoms as well as thermally accessible structures within kBT/unit area of the global minimum, with T = 300 K. Overall, the Genetic Algorithm is more robust with respect to parameter choice and in success rate, while the Basin Hopping method occasionally exhibits some advantages in speed of convergence. In line with previous studies, the results confirm that robustness, success, and speed of convergence of either approach are strongly influenced by how much the trial moves tend to preserve favorable bonding patterns once these appear.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Keywords: | GENETIC ALGORITHM,basin hopping,global optimization,SURFACE RECONSTRUCTION,Silicon |
Dates: |
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Institution: | The University of York |
Academic Units: | The University of York > Faculty of Sciences (York) > Physics (York) |
Depositing User: | Pure (York) |
Date Deposited: | 17 May 2022 15:00 |
Last Modified: | 16 Oct 2024 18:26 |
Published Version: | https://doi.org/10.1021/acs.jpca.2c00647 |
Status: | Published |
Refereed: | Yes |
Identification Number: | 10.1021/acs.jpca.2c00647 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:186995 |