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ICSD News

June 2018

ICSD now contains 199,466 crystal structures

June 2018

ICSD web version 4.0.0 released

January 2018

ICSD and Material Research

Featured article

Analysis of spinel compounds in ICSD

ICSD enables scientists to search for text in titles in combination with chemical compound information, e.g., compound classes which cannot be described through specific compound information such as the molecular formula. An interesting example for such a compound class is provided by the spinel compounds.

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Publications

Find out more information on history and usage of ICSD in our Brochure "A Focus on Crystallography":

Original publications to be cited in scientific work referring to the ICSD database:
Bergerhoff, G. & Brown, I.D. in „Crystallographic Databases“, F.H. Allen et al. (Hrsg.) Chester, International Union of Crystallography, (1987).

An updated view on the ICSD database:
Belsky, A., Hellenbrandt, M., Karen, V. L. & Luksch, P., Acta Cryst. B58 (2002), 364–369, „New developments in the Inorganic Crystal Structure Database (ICSD): accessibility in support of materials research and design “. http://dx.doi.org/10.1107/S0108768102006948

http://dx.doi.org/10.1107/S0108768102006948Allmann, R. & Hinek, R., Acta Cryst. A63 (2007), 412–417, „ The introduction of structure types into the Inorganic Crystal Structure Database ICSD “, http://dx.doi.org/10.1107/S0108767307038081

Applications of the ICSD database:
F. Meutzner, W. Münchgesang, T. Leisegang, R. Schmid, M. Zschornak, M. Ureña de Vivanco, A. P. Shevchenko, V. A. Blatov, D. C. Meyer, Crystal Research & Technology 52 (2017) 1600223, “Identification of solid oxygen‐containing Na‐electrolytes: An assessment based on crystallographic and economic parameters”. http://dx.doi.org/10.1002/crat.201600223

S. Kirklin, J. E. Saal, B. Meredig, A. Thompson, J. W. Doak, M. Aykol, S. Rühl, C. Wolverton, npj Computational Materials 1 (2015), 15010, “The Open Quantum Materials Database (OQMD): assessing the accuracy of DFT formation energies”.  http://dx.doi.org/10.1038/npjcompumats.2015.10

A. G. Kusne, T. Gao, A. Mehta, L. Ke, M. C. Nguyen, K.-M. Ho, V. Antropov, C.-Z. Wang, M. J. Kramer, C. Long, I. Takeuchi, Sci. Rep. 4 (2014), 6367 , “On-the-fly machine-learning for high-throughput experiments: search for rare-earth-free permanent magnets”. http://dx.doi.org/10.1038/srep06367

S. Yang, M. Lach-hab, I. I. Vaisman, E. Blaisten-Barojas, In Proceedings of the 2008 International Conference on Data Mining, CSREA: Las Vegas (2008), 702-706, “Machine Learning Approach for Classification of Zeolite Crystals”. S. Yang, M. Lach-hab, I. I. Vaisman, E. Blaisten-Barojas, J. Phys. Chem. C 113 (2009), 21721–21725. http://dx.doi.org/ 10.1021/jp907017u

Behrens, H., Luksch, P., Acta Cryst. B62 (2006), 993–1001, „ A bibliometric study in crystallography “, https://doi.org/10.1107/S0108768106030278

Kaduk, J.A., Acta Cryst. B58 (2002), 370–379, „Use of the Inorganic Crystal Structure Database as a problem solving tool“, https://doi.org/10.1107/S0108768102003476