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Licensed Unlicensed Requires Authentication Published by De Gruyter October 2, 2018

Computational analysis and identification of battery materials

  • F. Meutzner EMAIL logo , T. Nestler , M. Zschornak , P. Canepa , G. S. Gautam , S. Leoni , S. Adams , T. Leisegang , V. A. Blatov and D. C. Meyer
From the journal Physical Sciences Reviews

Abstract

Crystallography is a powerful descriptor of the atomic structure of solid-state matter and can be applied to analyse the phenomena present in functional materials. Especially for ion diffusion – one of the main processes found in electrochemical energy storage materials – crystallography can describe and evaluate the elementary steps for the hopping of mobile species from one crystallographic site to another. By translating this knowledge into parameters and search for similar numbers in other materials, promising compounds for future energy storage materials can be identified. Large crystal structure databases like the ICSD, CSD, and PCD have accumulated millions of measured crystal structures and thus represent valuable sources for future data mining and big-data approaches. In this work we want to present, on the one hand, crystallographic approaches based on geometric and crystal-chemical descriptors that can be easily applied to very large databases. On the other hand, we want to show methodologies based on ab initio and electronic modelling which can simulate the structure features more realistically, incorporating also dynamic processes. Their theoretical background, applicability, and selected examples are presented.

Acknowledgements

FM, TN, MZ, TL, and DCM are grateful for financial support of the Federal Ministry of Education and Research (CryPhysConcept (03EK3029A) and R2RBattery (03SF0542A)). PC is grateful to the Ramsey Memorial Trust for the provision of his Ramsey Fellowship. SL acknowledges support from the UK Research Council for using work in the paper that was undertaken by a student under Project No. EP/M50631X/. SL also thanks ARCCA Cardiff for computational resources. SA would like to thank National Research Foundation, Prime Minister’s Office, Singapore for support under its Competitive Research Programme (NRF-CRP 10-2012-6) and NUS for support under the “NUS Centre for Energy Research” grant. VAB is grateful for financial support of the Russian Megagrant (14.B25.31.0005), and the Russian Science Foundation (16-13-10158).

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Published Online: 2018-10-02

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