粒子群优化
晶体结构预测
计算机科学
数学优化
渡线
算法
能量最小化
趋同(经济学)
进化算法
晶体结构
数学
化学
计算化学
结晶学
人工智能
经济增长
经济
作者
Yanchao Wang,Jian Lv,Li Zhu,Yanming Ma
出处
期刊:Physical Review B
[American Physical Society]
日期:2010-09-28
卷期号:82 (9)
被引量:1860
标识
DOI:10.1103/physrevb.82.094116
摘要
We have developed a powerful method for crystal structure prediction from "scratch" through particle swarm optimization (PSO) algorithm within the evolutionary scheme. PSO technique is dramatically different with the genetic algorithm and has apparently avoided the use of evolution operators (e.g., crossover and mutation). The approach is based on a highly 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 factor method 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 structural 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 remarkable success rate demonstrates the reliability of this methodology and illustrates the great promise of PSO as a major technique on crystal structure determination.
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