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Adaptive Genetic Algorithm for Structure Prediction and Application to Magnetic Materials

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Handbook of Materials Modeling

Abstract

We present an adaptive genetic algorithm which can be used to predict the atomistic structures of crystals, interfaces, and nanoparticles based on the chemical composition of the materials. The method combines the speed of structure exploration by classical potentials with the accuracy of total energy calculations using first-principles density functional theory (DFT). This method increases the efficiency of structure prediction based on DFT calculations by several orders of magnitude and allows considerable increase in size and complexity of systems that can be studied. The performance of the method is demonstrated by successful structure identifications of complex binary and ternary intermetallic compounds with the number of atoms in the unit cell as large as 150 atoms. Applications of the method to discovery of novel magnetic materials are discussed.

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Acknowledgments

We are grateful to Dr. David Sellmyer, Dr. B. Balamurugan, and Dr. J. R. Chelikowsky for useful discussion and collaboration on magnetic structure prediction and discovery. We would like to acknowledge US Department of Energy, Basic Energy Sciences, Division of Materials Science and Engineering, who supported the development of adaptive genetic algorithm for crystal structure prediction. We also would like to acknowledge the support from the National Science Foundation (NSF), Division of Materials Research (DMR) under Award DMREF: SusChEM 1436386 and DMREF: SusChEM 1729677 to support the research on discovery of novel magnetic materials. Ames Laboratory is operated for the U.S. DOE by Iowa State University under contract # DE-AC02-07CH11358.

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Authors and Affiliations

  1. Ames Laboratory and Department of Physics, Iowa State University, Ames, IA, USA

    Xin Zhao, Manh Cuong Nguyen, Kai-Ming Ho & Cai-Zhuang Wang

  2. Department of Physics, Xiamen University, Xiamen, China

    Shunqing Wu

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  1. Xin Zhao
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  2. Shunqing Wu
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  3. Manh Cuong Nguyen
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  4. Kai-Ming Ho
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  5. Cai-Zhuang Wang
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Corresponding author

Correspondence to Cai-Zhuang Wang .

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Editors and Affiliations

  1. Institute of Physics, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland

    Wanda Andreoni

  2. Department of Nuclear Science & Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA

    Sidney Yip

Section Editor information

  1. Ames Laboratory and Department of Physics and Astronomy, Iowa State University, Ames, USA

    Cai-Zhuang Wang

  2. Northwestern University, Evanston, IL, USA

    Christopher M. Wolverton

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Zhao, X., Wu, S., Nguyen, M.C., Ho, KM., Wang, CZ. (2019). Adaptive Genetic Algorithm for Structure Prediction and Application to Magnetic Materials. In: Andreoni, W., Yip, S. (eds) Handbook of Materials Modeling. Springer, Cham. https://doi.org/10.1007/978-3-319-50257-1_73-1

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  • DOI: https://doi.org/10.1007/978-3-319-50257-1_73-1

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