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Performance of genetic algorithms in search for water splitting perovskites

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Abstract

We examine the performance of genetic algorithms (GAs) in uncovering solar water light splitters over a space of almost 19,000 perovskite materials. The entire search space was previously calculated using density functional theory to determine solutions that fulfill constraints on stability, band gap, and band edge position. Here, we test over 2500 unique GA implementations in finding these solutions to determine whether GA can avoid the need for brute force search, and thereby enable larger chemical spaces to be screened within a given computational budget. We find that the best GAs tested offer almost a 6 times efficiency gain over random search, and are comparable to the performance of a search based on informed chemical rules. In addition, the GA is almost 10 times as efficient as random search in finding half the solutions within the search space. By employing chemical rules, the performance of the GA can be further improved to approximately 12–17 better than random search. We discuss the effect of population size, selection function, crossover function, mutation rate, fitness function, and elitism on the final result, finding that selection function and elitism are especially important to GA performance. In addition, we determine that parameters that perform well in finding solar water splitters can also be applied to discovering transparent photocorrosion shields. Our results indicate that coupling GAs to high-throughput density functional calculations presents a promising method to rapidly search large chemical spaces for technological materials.

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Acknowledgements

We thank Dr. Shahar Keinan, Dr. Yosuke Kanai, Dr. Jeffrey Tilson, and Dr. Robert Fowler for their thoughts and assistance in designing this study. We thank Dr. Byron Schmuland for providing an elegant derivation of the random choosing probability problem via Math Exchange. Geoffroy Hautier acknowledges the F.R.S.- FNRS Belgium for financial support under a ‘‘Chargé de Recherche’’ grant. Anubhav Jain acknowledges funding through the U.S. Government under Contract DE-AC02-05CH11231 and the Luis W. Alvarez Fellowship in Computational Science. Ivano E. Castelli and Karsten W. Jacobsen acknowledge support from the Danish Center for Scientific Computing through grant HDW-1103-06, from the Catalysis for Sustainable Energy (CASE) initiative funded by the Danish Ministry of Science, Technology and Innovation and from the Center for Atomic-scale Materials Design (CAMD) sponsored by the Lundbeck Foundation. This research is supported by the Office of Science of the U.S. Department of Energy under contract DE-AC02-05CH11231.

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Authors and Affiliations

  1. Computational Research Division, Lawrence Berkeley National Laboratory, 1 Cyclotron Rd, Berkeley, CA, 94720, USA

    Anubhav Jain & David H. Bailey

  2. Center for Atomic-scale Materials Design, Department of Physics, Technical University of Denmark, 2800, Kongens Lyngby, Denmark

    Ivano E. Castelli & Karsten W. Jacobsen

  3. ETSF, Institute of Condensed Matter and Nanosciences, Université Catholique de Louvain, 1348, Louvain-la-Neuve, Belgium

    Geoffroy Hautier

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  1. Anubhav Jain
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  2. Ivano E. Castelli
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  5. Karsten W. Jacobsen
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Correspondence to Anubhav Jain.

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Jain, A., Castelli, I.E., Hautier, G. et al. Performance of genetic algorithms in search for water splitting perovskites. J Mater Sci 48, 6519–6534 (2013). https://doi.org/10.1007/s10853-013-7448-9

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