中文摘要：

凝聚态物质内部的原子排列方式,即结构,是深入理解其宏观物理和化学性质的重要信息.只根据物质的化学组分从理论上开展物质的结构预测是物理、化学和材料科学长期的期盼,但一直是个巨大挑战.基于结构对称性的分类检索思想,结合粒子群多目标优化算法,引入成键特征矩阵的结构表征方法,提出并发展了卡里普索（CALYPSO）结构预测方法,并在此基础上开发了拥有自主知识产权的同名结构预测软件包.该方法和软件只需给定材料的化学组分和外界条件（如压力）,就可以预测材料的基态及亚稳态结构,并可以进行功能材料逆向设计.CALYPSO方法的高效可靠性已经在科研实践中得到了证实.目前该方法已经被广泛应用到三维晶体、二维层状材料和表面、零维的团簇等体系的结构研究领域,成为理论确定材料结构的有效手段.

英文摘要：

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 samename 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.