Journal of Materials Science

, Volume 48, Issue 19, pp 6519–6534 | Cite as

Performance of genetic algorithms in search for water splitting perovskites

  • Anubhav Jain
  • Ivano E. Castelli
  • Geoffroy Hautier
  • David H. Bailey
  • Karsten W. Jacobsen


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.


Genetic Algorithm Perovskite Fitness Function Density Functional Theory Computation Random Search 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



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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Copyright information

© Springer Science+Business Media New York 2013

Authors and Affiliations

  • Anubhav Jain
    • 1
  • Ivano E. Castelli
    • 2
  • Geoffroy Hautier
    • 3
  • David H. Bailey
    • 1
  • Karsten W. Jacobsen
    • 2
  1. 1.Computational Research DivisionLawrence Berkeley National LaboratoryBerkeleyUSA
  2. 2.Center for Atomic-scale Materials Design, Department of PhysicsTechnical University of DenmarkKongens LyngbyDenmark
  3. 3.ETSF, Institute of Condensed Matter and NanosciencesUniversité Catholique de LouvainLouvain-la-NeuveBelgium

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