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Performance of genetic algorithms in search for water splitting perovskites

Bibliographic reference Anubhav Jain ; Ivano E. Castelli ; Hautier, Geoffroy ; David H. Bailey ; Karsten W. Jacobsen. Performance of genetic algorithms in search for water splitting perovskites. In: Journal of Materials Science, Vol. 48, no. 19, p. 6519-6534 (2013)
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  1. Hohenberg P., Kohn W., Inhomogeneous Electron Gas, 10.1103/physrev.136.b864
  2. Kohn W., Sham L. J., Self-Consistent Equations Including Exchange and Correlation Effects, 10.1103/physrev.140.a1133
  3. Hautier Geoffroy, Jain Anubhav, Ong Shyue Ping, From the computer to the laboratory: materials discovery and design using first-principles calculations, 10.1007/s10853-012-6424-0
  4. Hafner Jürgen, Wolverton Christopher, Ceder Gerbrand, Toward Computational Materials Design: The Impact of Density Functional Theory on Materials Research, 10.1557/mrs2006.174
  5. Castelli Ivano E., Landis David D., Thygesen Kristian S., Dahl Søren, Chorkendorff Ib, Jaramillo Thomas F., Jacobsen Karsten W., New cubic perovskites for one- and two-photon water splitting using the computational materials repository, 10.1039/c2ee22341d
  6. Castelli Ivano E., Olsen Thomas, Datta Soumendu, Landis David D., Dahl Søren, Thygesen Kristian S., Jacobsen Karsten W., Computational screening of perovskite metal oxides for optimal solar light capture, 10.1039/c1ee02717d
  7. Jain Anubhav, Hautier Geoffroy, Moore Charles J., Ping Ong Shyue, Fischer Christopher C., Mueller Tim, Persson Kristin A., Ceder Gerbrand, A high-throughput infrastructure for density functional theory calculations, 10.1016/j.commatsci.2011.02.023
  8. Materials Project (2011)
  9. Holland JH (1975) Adaptation in natural and artificial systems. University of Michigan Press, Ann Arbor
  10. Hautier Geoffroy, Fischer Chris, Ehrlacher Virginie, Jain Anubhav, Ceder Gerbrand, Data Mined Ionic Substitutions for the Discovery of New Compounds, 10.1021/ic102031h
  11. Hautier Geoffroy, Fischer Christopher C., Jain Anubhav, Mueller Tim, Ceder Gerbrand, Finding Nature’s Missing Ternary Oxide Compounds Using Machine Learning and Density Functional Theory, 10.1021/cm100795d
  12. Balachandran P. V., Broderick S. R., Rajan K., Identifying the 'inorganic gene' for high-temperature piezoelectric perovskites through statistical learning, 10.1098/rspa.2010.0543
  13. Franceschetti Alberto, Zunger Alex, 10.1038/46995
  14. Kim Kwiseon, Graf Peter A., Jones Wesley B., A genetic algorithm based inverse band structure method for semiconductor alloys, 10.1016/
  15. Dudiy S. V., Zunger Alex, Searching for Alloy Configurations with Target Physical Properties: Impurity Design via a Genetic Algorithm Inverse Band Structure Approach, 10.1103/physrevlett.97.046401
  16. d’Avezac Mayeul, Luo Jun-Wei, Chanier Thomas, Zunger Alex, Genetic-Algorithm Discovery of a Direct-Gap and Optically Allowed Superstructure from Indirect-Gap Si and Ge Semiconductors, 10.1103/physrevlett.108.027401
  17. Jóhannesson G. H., Bligaard T., Ruban A. V., Skriver H. L., Jacobsen K. W., Nørskov J. K., Combined Electronic Structure and Evolutionary Search Approach to Materials Design, 10.1103/physrevlett.88.255506
  18. Graf Peter A., Kim Kwiseon, Jones Wesley B., Hart Gus L. W., Direct enumeration of alloy configurations for electronic structural properties, 10.1063/1.2142091
  19. Piquini Paulo, Graf Peter A., Zunger Alex, Band-Gap Design of Quaternary (In,Ga)(As,Sb) Semiconductors via the Inverse-Band-Structure Approach, 10.1103/physrevlett.100.186403
  20. Chakraborti N., Genetic algorithms in materials design and processing, 10.1179/095066004225021909
  21. Bhalla A. S., Guo R., Roy R., The perovskite structure - a review of its role in ceramic science and technology, 10.1007/s100190000062
  22. Peña M. A., Fierro J. L. G., Chemical Structures and Performance of Perovskite Oxides, 10.1021/cr980129f
  23. Landis David D., Hummelshoj Jens S., Nestorov Svetlozar, Greeley Jeff, Dulak Marcin, Bligaard Thomas, Norskov Jens K., Jacobsen Karsten W., The Computational Materials Repository, 10.1109/mcse.2012.16
  24. Computational Materials Repository (2013)
  25. Enkovaara J, Rostgaard C, Mortensen J J, Chen J, Dułak M, Ferrighi L, Gavnholt J, Glinsvad C, Haikola V, Hansen H A, Kristoffersen H H, Kuisma M, Larsen A H, Lehtovaara L, Ljungberg M, Lopez-Acevedo O, Moses P G, Ojanen J, Olsen T, Petzold V, Romero N A, Stausholm-Møller J, Strange M, Tritsaris G A, Vanin M, Walter M, Hammer B, Häkkinen H, Madsen G K H, Nieminen R M, Nørskov J K, Puska M, Rantala T T, Schiøtz J, Thygesen K S, Jacobsen K W, Electronic structure calculations with GPAW: a real-space implementation of the projector augmented-wave method, 10.1088/0953-8984/22/25/253202
  26. Mortensen J. J., Hansen L. B., Jacobsen K. W., Real-space grid implementation of the projector augmented wave method, 10.1103/physrevb.71.035109
  27. Hammer B., Hansen L. B., Nørskov J. K., Improved adsorption energetics within density-functional theory using revised Perdew-Burke-Ernzerhof functionals, 10.1103/physrevb.59.7413
  28. Kuisma M., Ojanen J., Enkovaara J., Rantala T. T., Kohn-Sham potential with discontinuity for band gap materials, 10.1103/physrevb.82.115106
  29. Gritsenko Oleg, van Leeuwen Robert, van Lenthe Erik, Baerends Evert Jan, Self-consistent approximation to the Kohn-Sham exchange potential, 10.1103/physreva.51.1944
  30. Xu Yong, Schoonen Martin A.A., The absolute energy positions of conduction and valence bands of selected semiconducting minerals, 10.2138/am-2000-0416
  31. Butler M. A., Prediction of Flatband Potentials at Semiconductor-Electrolyte Interfaces from Atomic Electronegativities, 10.1149/1.2131419
  32. Armiento Rickard, Kozinsky Boris, Fornari Marco, Ceder Gerbrand, Screening for high-performance piezoelectrics using high-throughput density functional theory, 10.1103/physrevb.84.014103
  33. Oganov Artem R., Lyakhov Andriy O., Valle Mario, How Evolutionary Crystal Structure Prediction Works—and Why, 10.1021/ar1001318
  34. Sastry Kumara, Goldberg David, Kendall Graham, Genetic Algorithms, Search Methodologies ISBN:9780387234601 p.97-125, 10.1007/0-387-28356-0_4
  35. Goldberg D (1989) Genetic algorithms in search, optimization, and machine learning. Addison Wesley, Reading
  36. Perone C (2012) Pyevolve software
  37. Perone Christian S., Pyevolve : a Python open-source framework for genetic algorithms, 10.1145/1656395.1656397
  38. Konak Abdullah, Coit David W., Smith Alice E., Multi-objective optimization using genetic algorithms: A tutorial, 10.1016/j.ress.2005.11.018
  39. Mercer RE (1977) Adaptive search using a reproductive meta-plan. University of Alberta, Edmonton
  40. Grefenstette John, Optimization of Control Parameters for Genetic Algorithms, 10.1109/tsmc.1986.289288
  41. Sastry Kumara, Abbass Hussein A., Goldberg David E., Johnson D. D., Sub-structural niching in estimation of distribution algorithms, 10.1145/1068009.1068123
  42. Perry ZA (1984) Experimental study of speciation in ecological niche theory using genetic algorithms. Doctoral Thesis, University of Michigan
  43. Mauldin M (1984) In: Proceedings of the national conference on artificial intelligence, Austin, TX, p 247
  44. Goldberg DE, Richardson J (1987) In: Proceedings of the second international conference on genetic algorithms, p 41
  45. Goldschmidt V. M., Die Gesetze der Krystallochemie, 10.1007/bf01507527
  46. Schmuland B (2012) Math Exchange Forum.
  47. Fisher R. A., Theory of Statistical Estimation, 10.1017/s0305004100009580
  48. Rojas I., Gonzalez J., Pomares H., Merelo J.J., Castillo P.A., Romero G., Statistical analysis of the main parameters involved in the design of a genetic algorithm, 10.1109/tsmcc.2002.1009128
  49. Sahai H, Ageel MI (2000) The analysis of variance: fixed, random and mixed models. Birkhäuser, Boston
  50. Calle-Vallejo Federico, Martínez José I., García-Lastra Juan M., Mogensen Mogens, Rossmeisl Jan, Trends in Stability of Perovskite Oxides, 10.1002/anie.201002301
  51. Holland J (1968) Hierarchical descriptions of universal spaces and adaptive systems. Technical Report, University of Michigan, Department of Computer and Communication Sciences
  52. Berger Robert F., Neaton Jeffrey B., Computational design of low-band-gap double perovskites, 10.1103/physrevb.86.165211
  53. Wu Y, Lazic P, Hautier G, Persson K, Ceder G (2013) Energy Environ Sci 6:157
  54. Oganov Artem R., Glass Colin W., Crystal structure prediction using ab initio evolutionary techniques: Principles and applications, 10.1063/1.2210932
  55. Woodley S (2004) Appl Evol Comput Chem 110:95
  56. Bethke AD (1976) Comparison of genetic algorithms and gradient-based optimizers on parallel processors: efficiency of use of processing capacity. Technical Report, University of Michigan, Logic of Computers Group
  57. Cantu-Paz E (2000) Efficient and accurate parallel genetic algorithms. Springer, New York
  58. Bandow Bernhard, Hartke Bernd, Larger Water Clusters with Edges and Corners on Their Way to Ice:  Structural Trends Elucidated with an Improved Parallel Evolutionary Algorithm, 10.1021/jp060512l
  59. Munter T R, Landis D D, Abild-Pedersen F, Jones G, Wang S, Bligaard T, Virtual materials design using databases of calculated materials properties, 10.1088/1749-4699/2/1/015006
  60. Ortiz C., Eriksson O., Klintenberg M., Data mining and accelerated electronic structure theory as a tool in the search for new functional materials, 10.1016/j.commatsci.2008.07.016
  61. Balamurugan D., Yang Weitao, Beratan David N., Exploring chemical space with discrete, gradient, and hybrid optimization methods, 10.1063/1.2987711
  62. Anatole von Lilienfeld O., Accurate ab initio energy gradients in chemical compound space, 10.1063/1.3249969
  63. Wang Mingliang, Hu Xiangqian, Beratan David N., Yang Weitao, Designing Molecules by Optimizing Potentials, 10.1021/ja0572046