CALYPSO: A method for crystal structure prediction

By Yanchao Wang, Jian Lv, Li Zhu, Yanming Ma. Computer Physics Communications, 2012

2,422 citations recorded by the source corpus.

We have developed a software package CALYPSO (Crystal structure AnaLYsis by Particle Swarm Optimization) to predict the energetically stable/metastable crystal structures of materials at given chemical compositions and external conditions (e.g., pressure). The CALYPSO method is based on several major techniques (e.g. particle-swarm optimization algorithm, symmetry constraints on structural generation, bond characterization matrix on elimination of similar structures, partial random structures per generation on enhancing structural diversity, and penalty function, etc.) for global structural minimization from scratch. All of these techniques have been demonstrated to be critical to the prediction of global stable structure. We have implemented these techniques into the CALYPSO code. Testing of the code on many known and unknown systems shows high efficiency and the highly successful rate of this CALYPSO method [Y. Wang, J. Lv, L. Zhu, Y. Ma, Phys. Rev. B 82 (2010) 094116] [29]. In this paper, we focus on descriptions of the implementation of CALYPSO code and why it works.

Subfields: Materials Chemistry, Mechanics of Materials, Physical and Theoretical Chemistry

View Paper: https://www.semanticscholar.org/paper/cf50e5445ff0163170f06d0093082aab3fa9722a

How researchers see this paper

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In Materials Chemistry

1. Linfeng Zhang
2. David J. Srolovitz
3. W. E
4. T. Wen
5. Han Wang
6. J. A. Flores-Livas
7. Roald Hoffmann
8. Ivan I. Naumov

In Mechanics of Materials

1. David J. Srolovitz
2. Nicholas K.-R. Kevlahan

In Physical and Theoretical Chemistry

1. Han Wang
2. Linfeng Zhang
3. W. E
4. David J. Srolovitz
5. T. Wen
6. Hanyu Liu

Citations in the corpus

• Potential high-Tc superconducting lanthanum and yttrium hydrides at high pressure (2017, methodology): “The search for low-energy crystalline structures of La–H was performed using particle swarm optimization methodology implemented in the CALYPSO code (31, 32).”
• Deep Potentials for Materials Science (2022, methodology): “This work interfaces the DeePMD-kit package and crystal structure prediction software (e.g., CALYPSO [210], USPEX [75], and Pychemia [211]).”
• A perspective on conventional high-temperature superconductors at high pressure: Methods and materials (2019, background): “Particle Swarm Optimization exists in two variants, based on global and local PSO algorithms, which have both been successfully used in many applications [284].”