  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.
