University of Cambridge > > Theory of Condensed Matter > CALYPSO: A Useful Tool for Discovering Novel Structures and Properties of Materials at Extreme Conditions

CALYPSO: A Useful Tool for Discovering Novel Structures and Properties of Materials at Extreme Conditions

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Atomistic structures of materials occupy a central and often critical role, when establishing a correspondence between materials performance and their compositions. Therefore, theoretical prediction of atomistic structures of materials with the only given information of chemical compositions play a vital role in materials design, but it is extremely difficult as it basically involves in classifying a huge number of energy minima on the lattice energy surface. We have developed an efficient CALYPSO approach [1-2] for structure prediction from “scratch” based on swarm optimization algorithm by taking the advantage of swarm intelligence and the spirit of structures smart learning. Symmetry constraints on structure generations, bond characterization matrix for fingerprinting structures, and geometric optimization are other irreplaceable ingredients of CALYPSO [1-2]. The method has been coded into CALYPSO software (

In this presentation, I will give a short introduction into the principle of CALYPSO structure prediction method [1-2]. Primary emphasis will be placed on its application into exploration of novel high-pressure structures with exotic physics and chemistry not accessible to ambient pressure. Examples will be presented towards to the prediction and its later experimental confirmation of insulating electride lithium [3], out-of-imagination cage structure of polymeric nitrogen [4], and unexpected chemical reactions of Fe and Xe, and Ca and H2 at extreme conditions with the formation of unusual stoichiometries. Reaction of Fe and Xe in the conditions of Earth core [5] might provide a possible solution of “missing Xe paradox”. Reaction of Ca and H2 brings element one into sodalite cage [6].

[1] Y. Wang, J. Lv, L.Zhu, and Y. Ma, Phys. Rev. B 82 , 094116 (2010). [2] Y. Wang, J. Lv, L.Zhu, and Y. Ma, Comput. Phys. Commun. 183, 2063 (2012). [3] J. Lv, Y. Wang, L. Zhu, and Y. Ma, Phys. Rev. Lett. 106, 015503 (2011). [4] X. Wang, et al., Phys. Rev. Lett. 109, 175502 (2012). [5] L. Zhu, et al., Nature Chemistry, doi:10.1038/nchem.1925 (2014). [6] H. Wang, J. Tse, K. Tanaka, T. Iitaka, and Y. Ma, Proc. Natl. Acad. Sci. USA , 109, 6463 (2012).

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