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CALYPSO Method for Structure Prediction and Its Applications to Materials Discovery

  • Yanchao WangEmail author
  • Jian Lv
  • Quan Li
  • Hui Wang
  • Yanming Ma
Living reference work entry

Abstract

Atomic-level structure prediction for condensed matters, given only a chemical composition, is a major challenging issue in a broad range of science (e.g., physics, chemistry, materials, and planetary science, etc.). By combining the global swarm optimization algorithm with a number of specially designed structure-dealing techniques (e.g., symmetry constraints, structure fingerprints, etc.), we developed the CALYPSO (Crystal structure AnaLYsis by Particle Swarm Optimization) structure prediction method that is able to predict the structures of a wide range of materials including isolated clusters/nanoparticles, two-dimensional layers and reconstructed surfaces, and three-dimensional bulks and holds the promise for the functionality-driven design of materials (e.g., superhard, electride, and optical materials, etc.). It has been demonstrated in a wide range of applications that CALYPSO is highly efficient when searching for the structures of materials and becomes an invaluable tool for aiding materials discovery. In this chapter, we provide an overview of the basic theory and main features of the CALYPSO approach, as well as its versatile applicability to the design of superconductors and superhard materials. Finally, the conclusion and opportunities for further research on CALYPSO method are presented.

Notes

Acknowledgments

The authors acknowledge funding support from the National Key Research and Development Program of China under Grant No. 2016YFB0201200, No. 2016YFB0201201, and No. 2017YFB0701503; NSAF (No. U1530124)? the National Natural Science Foundation of China under Grants No. 11774127, No. 11534003, No. 11622432 and No. 11722433); supported by Program for JLU Science and Technology Innovative Research Team (JLUSTIRT); and the Science Challenge Project, No. TZ2016001.

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Yanchao Wang
    • 1
    Email author
  • Jian Lv
    • 1
  • Quan Li
    • 1
    • 2
  • Hui Wang
    • 1
  • Yanming Ma
    • 1
    • 2
  1. 1.State Key Laboratory of Superhard Materials & Innovation Center of Computational Physics Methods and Software, College of PhysicsJilin UniversityChangchunChina
  2. 2.International Center of Future ScienceJilin UniversityChangchunChina

Section editors and affiliations

  • Cai-Zhuang Wang
    • 1
  • Christopher M. Wolverton
    • 2
  1. 1.Ames Laboratory and Department of Physics and AstronomyIowa State UniversityAmesUSA
  2. 2.Northwestern UniversityEvanstonUSA

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