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Computational analysis and identification of battery materials

F. Meutzner
  • Corresponding author
  • TU Bergakademie Freiberg, Institut für Experimentelle Physik, Leipziger Str. 23, Freiberg 09596, Germany
  • Samara National Research University, Moskovskoye Shosse 34, Samara 443086, Russian Federation
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  • De Gruyter OnlineGoogle Scholar
/ T. Nestler / M. Zschornak
  • TU Bergakademie Freiberg, Institut für Experimentelle Physik, Leipziger Str. 23, Freiberg 09596, Germany
  • Helmholtz-Zentrum Dresden-Rossendorf e.V. (HZDR), Institute of Ion Beam Physics & Materials Research, Bautzner Landstraße 400, Dresden 01328, Germany
  • Email
  • Other articles by this author:
  • De Gruyter OnlineGoogle Scholar
/ P. Canepa / G. S. Gautam / S. Leoni / S. Adams
  • Department of Materials Science & Engineering, National University of Singapore, Engineering Drive 2, 117579 Singapore, Singapore
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  • De Gruyter OnlineGoogle Scholar
/ T. Leisegang
  • TU Bergakademie Freiberg, Institut für Experimentelle Physik, Leipziger Str. 23, Freiberg 09596, Germany
  • Samara National Research University, Moskovskoye Shosse 34, Samara 443086, Russian Federation
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  • De Gruyter OnlineGoogle Scholar
/ V. A. Blatov / D. C. Meyer
Published Online: 2018-10-02 | DOI: https://doi.org/10.1515/psr-2018-0044


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.

Keywords: crystallography; electrochemistry; Voronoi–Dirichlet partitioning; bond valence sum; density functional theory


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Published Online: 2018-10-02

Citation Information: Physical Sciences Reviews, 20180044, ISSN (Online) 2365-659X, DOI: https://doi.org/10.1515/psr-2018-0044.

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