Crystal structure prediction via particle-swarm optimization

TLDR

PSO differs from genetic algorithms by avoiding crossover and mutation operators. The authors developed a crystal‑structure prediction method from scratch using particle‑swarm optimization within an evolutionary framework. The method performs efficient global minimization of free‑energy surfaces by integrating total‑energy calculations with PSO, employing a geometry‑based parameter to eliminate redundant structures, and applying symmetry constraints to reduce the search space and accelerate convergence. The variable unit‑cell size technique lowered computational cost, and the PSO algorithm successfully predicted numerous known elemental, binary, and ternary systems across metallic, ionic, and covalent bonding, achieving a high success rate that underscores its reliability and promise for crystal‑structure determination.

Abstract

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.