CALYPSO structure prediction method

Abstract

Microscopic structures of materials are fundamental basis for the understanding of physical and chemical properties, and they are also the key for design of functional materials. The theoretical prediction of structures with the only known information of chemical composition independent of previous experimental knowledge remains a great difficulty and a big challenge as it basically involves in classifying a huge number of energy minima on the lattice energy surface. We here have proposed a CALYPSO methodology for structure prediction based on several major techniques (e.g. particle-swarm optimization algorithm, symmetry constraints on structural generation, bond characterization matrix on elimination of similar structures, etc.) for global structural minimization and its same- name computer software. Our method allows the users to perform unbiased search of the energetically stable/metastable structures at given chemical compositions and design novel functional materials with desirable functionalities. Currently, our method has been applied to predict the structure of a broad range of materials including those of three-dimensional bulks, two-dimensional reconstructed surfaces and layers, and isolated clusters/nanoparticles or molecules. The high success rate demonstrates the reliability of this methodology and illustrates the promise of CALYPSO as a major technique on structure determination.