Crystal structure prediction via particle-swarm optimization

By Yanchao Wang, Jian Lv, Li Zhu, Yanming Ma. 2010

1,881 citations recorded by the source corpus.

We have developed a method for crystal structure prediction from ``scratch'' through particle-swarm optimization (PSO) algorithm within the evolutionary scheme. PSO technique is different with the genetic algorithm and has apparently avoided the use of evolution operators (e.g., crossover and mutation). The approach is based on an efficient global minimization of free-energy surfaces merging total-energy calculations via PSO technique and requires only chemical compositions for a given compound to predict stable or metastable structures at given external conditions (e.g., pressure). A particularly devised geometrical structure parameter which allows the elimination of similar structures during structure evolution was implemented to enhance the structure search efficiency. The application of designed variable unit-cell size technique has greatly reduced the computational cost. Moreover, the symmetry constraint imposed in the structure generation enables the realization of diverse structures, leads to significantly reduced search space and optimization variables, and thus fastens the global structure convergence. The PSO algorithm has been successfully applied to the prediction of many known systems (e.g., elemental, binary, and ternary compounds) with various chemical-bonding environments (e.g., metallic, ionic, and covalent bonding). The high success rate demonstrates the reliability of this methodology and illustrates the promise of PSO as a major technique on crystal structure determination.

Subfields: Computational Theory and Mathematics, Materials Chemistry, Physical and Theoretical Chemistry

View Paper: https://www.semanticscholar.org/paper/bb6318d515844b33b582c869b3f9924a5093616a

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In Computational Theory and Mathematics

In Materials Chemistry

In Physical and Theoretical Chemistry

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).”
First principles design of spintronics materials (2016, methodology): “Meanwhile, recently developed crystal structure search methods such as USPEX [149] and CALYPSO [150] provide us with very powerful tools.”
Microscopic theory of hardness and design of novel superhard crystals (2012, methodology): “Combining the microscopic hardness models with evolutionary searching algorithms of crystal structures [62,176], numerous superhard materials have been designed theoretically.”
Superconductive sodalite-like clathrate calcium hydride at high pressures (2012, methodology): “The search for stable CaH2n (n 1⁄4 2–6) structures at high pressures was performed using the particle-swam optimization structure prediction algorithm (19) in combination with ab initio calculations.”
The Properties of Hydrogen and Helium Under Extreme Conditions (2011, methodology): “Very recently, an alternative to AIRSS, the particleswarm optimization (PSO) method for structure prediction (Wang et al., 2010), was applied to dense hydrogen (Liu et al., 2012) and found a another stable H2 phase beyond Cmca in the pressure range 470 – 590 GPa.”