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软件地址： http://www.calypso.cn/
目前使用CALYPSO发表的SCI论文已高达260多篇，其中包括 Nature Chem, Nature Commun, PRL等
CALYPSO (Crystal structure AnaLYsis by Particle Swarm Optimization) is an efficient structure prediction method and its same-name computer software. The approach requires only chemical compositions for a given compound to predict stable or metastable structures at given external conditions (e.g., pressure), thus the CALYPSO package can be used to predict/determine the crystal structure and design the multi-functional materials (e.g., superhard). The CALYPSO package is protected by the Copyright Protection Center of China with the registration No. 2010SR028200 and classification No. 61000-7500.
WHAT IS THE FEATURE?
Predictions of the energetically stable/metastable structures at given chemical compositions and external conditions (e.g., pressure) for clusters, 2D layers, surfaces, and 3D crystals.
Design of novel functional materials, e.g., superhard materials.
Options for the structural evolutions using global or local PSO.
Structure searches with automatic variation of chemical compositions.
Structure predictions with fixed cell parameters, or fixed space groups, or fixed molecules.
CALYPSO is currently interfaced with VASP, CASTEP, Quantum Espresso, GULP, SIESTA and CP2K codes. The interface with other total energy codes can also be implemented by users' request.
Major Techniques Employed
The success of CALYPSO method is on account of the integration of several major techniques:
Structural evolution through PSO algorithm. PSO is best-known for its ability to conquer large barriers of energy landscapes by making use of the swarm intelligence and by self-improving structures. Both global and local PSO algorithms have been implemented. The global PSO has the advantage of fast convergence, while local PSO is good at avoiding premature convergence and thus enhance the capability of CALYPSO in dealing with more complex systems.
Symmetry constraints during structure generation to reduce searching space and enhance the structural diversity.
Structural characterization techniques to eliminate similar structures, define nonflying areas, enhance searching efficiency, and divide energy surfaces for local PSO searching.
(i) bond characterization matrix technique
(ii) atom-centered symmetrical function technique
Introducing new structures per generation with controllable percentage to enhance structural diversity.
Interface to a number of local structural optimization codes varying from highly accurate DFT methods to fast semiempirical approaches that can deal with large systems. Local structural optimization enables the reduction of noise of energy surfaces and the generation of physically justified structures.
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发表于 Post on 2016-4-25 21:07:54
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