CALYPSO: A method for crystal structure prediction

We have developed a software package CALYPSO (Crystal structure AnaLYsis by Particle Swarm Optimization) to predict the energetically stable/metastable crystal structures of materials at given chemical compositions and external conditions (e.g., pressure). The CALYPSO method is based on several major techniques (e.g. particle-swarm optimization algorithm, symmetry constraints on structural generation, bond characterization matrix on elimination of similar structures, partial random structures per generation on enhancing structural diversity, and penalty function, etc.) for global structural minimization from scratch. All of these techniques have been demonstrated to be critical to the prediction of global stable structure. We have implemented these techniques into the CALYPSO code. Testing of the code on many known and unknown systems shows high efficiency and the highly successful rate of this CALYPSO method [Y. Wang, J. Lv, L. Zhu, Y. Ma, Phys. Rev. B 82 (2010) 094116] [29]. In this paper, we focus on descriptions of the implementation of CALYPSO code and why it works.

[1]  Russell C. Eberhart,et al.  A discrete binary version of the particle swarm algorithm , 1997, 1997 IEEE International Conference on Systems, Man, and Cybernetics. Computational Cybernetics and Simulation.

[2]  S. Sinogeikin,et al.  Cold melting and solid structures of dense lithium , 2011 .

[3]  Xingao Gong,et al.  Ordered Semiconducting Nitrogen-Graphene Alloys , 2011, 1105.3540.

[4]  A. Laio,et al.  Assessing the accuracy of metadynamics. , 2005, The journal of physical chemistry. B.

[5]  Stefan Goedecker,et al.  Crystal structure prediction using the minima hopping method. , 2010, The Journal of chemical physics.

[6]  Howard R. Mayne,et al.  Global geometry optimization of atomic clusters using a modified genetic algorithm in space‐fixed coordinates , 1996 .

[7]  Chris J Pickard,et al.  Ab initio random structure searching , 2011, Journal of physics. Condensed matter : an Institute of Physics journal.

[8]  Peter Harrowell,et al.  Ordered binary crystal phases of Lennard-Jones mixtures. , 2004, The Journal of chemical physics.

[9]  Hui Wang,et al.  Substitutional alloy of Bi and Te at high pressure. , 2011, Physical review letters.

[10]  Yanming Ma,et al.  Absence of superconductivity in the high-pressure polymorph of MgB 2 , 2009 .

[11]  Ho,et al.  Molecular geometry optimization with a genetic algorithm. , 1995, Physical review letters.

[12]  D. Sánchez-Portal,et al.  The SIESTA method for ab initio order-N materials simulation , 2001, cond-mat/0111138.

[13]  Matt Probert,et al.  A periodic genetic algorithm with real-space representation for crystal structure and polymorph prediction , 2006, cond-mat/0605066.

[14]  Mario Valle,et al.  Transparent dense sodium , 2009, Nature.

[15]  Yanming Ma,et al.  Novel high pressure structures of polymeric nitrogen. , 2009, Physical review letters.

[16]  Yanchao Wang,et al.  Crystal structure prediction via particle-swarm optimization , 2010 .

[17]  Chris J Pickard,et al.  Highly compressed ammonia forms an ionic crystal. , 2008, Nature materials.

[18]  Chris J. Pickard,et al.  Structure of phase III of solid hydrogen , 2007 .

[19]  Chris J Pickard,et al.  High-pressure phases of silane. , 2006, Physical review letters.

[20]  J. Doye,et al.  Global Optimization by Basin-Hopping and the Lowest Energy Structures of Lennard-Jones Clusters Containing up to 110 Atoms , 1997, cond-mat/9803344.

[21]  Chris J Pickard,et al.  Dense low-coordination phases of lithium. , 2009, Physical review letters.

[22]  Yanming Ma,et al.  Superhard polymorphs of diamond-like BC7 , 2011 .

[23]  Xiaojun Wu,et al.  Predicting two-dimensional boron-carbon compounds by the global optimization method. , 2011, Journal of the American Chemical Society.

[24]  Matt Probert,et al.  First principles methods using CASTEP , 2005 .

[25]  F. Jensen.  Introduction to Computational Chemistry , 1998 .

[26]  G. Kresse,et al.  Efficiency of ab-initio total energy calculations for metals and semiconductors using a plane-wave basis set , 1996 .

[27]  John S. Tse,et al.  Metastable high-pressure single-bonded phases of nitrogen predicted via genetic algorithm , 2008 .

[28]  Matt Probert,et al.  First-principles simulation: ideas, illustrations and the CASTEP code , 2002 .

[29]  Yuhui Shi,et al.  Particle swarm optimization: developments, applications and resources , 2001, Proceedings of the 2001 Congress on Evolutionary Computation (IEEE Cat. No.01TH8546).

[30]  Yanming Ma,et al.  Superhard and superconductive polymorphs of diamond-like BC3 , 2011 .

[31]  Julian D. Gale,et al.  Part 1: Using a genetic algorithm and an indirect approach to exclusion zones , 2004 .

[32]  J. Pannetier,et al.  Prediction of crystal structures from crystal chemistry rules by simulated annealing , 1990, Nature.

[33]  Yanming Ma,et al.  Crystal Structures and Exotic Behavior of Magnesium under Pressure , 2010 .

[34]  Alex Zunger,et al.  Global space-group optimization problem : Finding the stablest crystal structure without constraints , 2007 .

[35]  Nikolaus Hansen,et al.  USPEX - Evolutionary crystal structure prediction , 2006, Comput. Phys. Commun..

[36]  Yanming Ma,et al.  Predicted novel high-pressure phases of lithium. , 2011, Physical review letters.

[37]  Julian D. Gale,et al.  GULP: A computer program for the symmetry-adapted simulation of solids , 1997 .

[38]  Kresse,et al.  Efficient iterative schemes for ab initio total-energy calculations using a plane-wave basis set. , 1996, Physical review. B, Condensed matter.

[39]  Gisbert Schneider,et al.  Optimized Particle Swarm Optimization (OPSO) and its application to artificial neural network training , 2006, BMC Bioinformatics.

[40]  Katsuya Shimizu,et al.  Direct observation of a pressure-induced metal-to-semiconductor transition in lithium , 2009, Nature.

[41]  P. Steinhardt,et al.  Bond-orientational order in liquids and glasses , 1983 .

[42]  David C. Lonie,et al.  XtalOpt: An open-source evolutionary algorithm for crystal structure prediction , 2011, Comput. Phys. Commun..

[43]  Roald Hoffmann,et al.  Structures and Potential Superconductivity in SiH~4 at High Pressure: En Route to "Metallic Hydrogen" , 2006 .

[44]  Paulo Cortez,et al.  Particle swarms for feedforward neural network training , 2002, Proceedings of the 2002 International Joint Conference on Neural Networks. IJCNN'02 (Cat. No.02CH37290).

[45]  Hui Wang,et al.  Superhard monoclinic polymorph of carbon. , 2009, Physical review letters.

[46]  S. Woodley,et al.  Structure prediction of titania phases : Implementation of Darwinian versus Lamarckian concepts in an Evolutionary Algorithm , 2009 .

[47]  C. D. Gelatt,et al.  Optimization by Simulated Annealing , 1983, Science.