The CALYPSO methodology for structure prediction

doi:10.1088/1674-1056/ab4174

Abstract Structure prediction methods have been widely used as a state-of-the-art tool for structure searches and materials discovery, leading to many theory-driven breakthroughs on discoveries of new materials. These methods generally involve the exploration of the potential energy surfaces of materials through various structure sampling techniques and optimization algorithms in conjunction with quantum mechanical calculations. By taking advantage of the general feature of materials potential energy surface and swarm-intelligence-based global optimization algorithms, we have developed the CALYPSO method for structure prediction, which has been widely used in fields as diverse as computational physics, chemistry, and materials science. In this review, we provide the basic theory of the CALYPSO method, placing particular emphasis on the principles of its various structure dealing methods. We also survey the current challenges faced by structure prediction methods and include an outlook on the future developments of CALYPSO in the conclusions.

Keywords: structure prediction CALYPSO method crystal structure potential energy surface

Received: 30 July 2019
Revised: 28 August 2019
Accepted manuscript online:

PACS:	61.50.Ah	(Theory of crystal structure, crystal symmetry; calculations and modeling)
	31.50.-x	(Potential energy surfaces)
	02.60.Pn	(Numerical optimization)

Fund: Project supported by the National Natural Science Foundation of China (Grant Nos. 11534003 and 11604117), the National Key Research and Development Program of China (Grant No. 2016YFB0201201), the Program for JLU Science and Technology Innovative Research Team (JLUSTIRT) of China, and the Science Challenge Project of China (Grant No. TZ2016001).

Corresponding Authors: Jian Lv, Yanchao Wang
E-mail: lvjian@calypso.cn;wyc@calypso.cn

Cite this article:

Qunchao Tong(童群超), Jian Lv(吕健), Pengyue Gao(高朋越), Yanchao Wang(王彦超) The CALYPSO methodology for structure prediction 2019 Chin. Phys. B 28 106105

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