基于粒子群优化算法的晶体结构预测
Crystal Structure Prediction via Particle Swarm Optimization
摘要：
我们已经开发了一个强大的方法，晶体结构预测从“零开始”，通过粒子群优化（PSO）算法的进化计划。PSO技术与遗传算法有很大的不同，并且显然避免了使用进化算子（例如，交叉和突变）。该方法基于通过PSO技术合并总能量计算的自由能表面的高效全局最小化，并且仅需要给定化合物的化学组成来预测给定外部条件下的稳定或亚稳结构（例如，压力）。为了提高结构搜索效率，提出了一种几何结构因子法，该方法允许在结构演化过程中消除相似结构。设计的变晶胞尺寸技术的应用大大降低了计算量。此外，在结构生成中施加的对称约束使得能够实现不同的结构，导致显着减少搜索空间和优化变量，从而避免了全局结构收敛。PSO算法已经成功地应用于许多已知系统的预测（例如，元素、二元和三元化合物）与各种化学键合环境（例如，金属、离子和共价键合）。显着的成功率证明了这种方法的可靠性，并说明了粒子群算法作为晶体结构测定的主要技术的巨大潜力。
英文摘要：
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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