About ICSD

FIZ Karlsruhe provides the scientific and the industrial community with ICSD (Inorganic Crystal Structure Database), the world's largest database for completely determined inorganic crystal structures.

The ICSD contains an almost exhaustive list of known inorganic crystal structures published since 1913, including their atomic coordinates. ICSD data is comprehensive, curated and is the perfect basis for finding answers on questions in materials research.

The database contains the following types of crystal structures:

• Experimental inorganic structures, which can be:
  either fully characterized where the atomic coordinates are determined and the composition is fully specified
  or the structure is published with a structure type so that the atomic coordinates and other parameters can be derived from existing data.
• Experimental metal-organic structures
  only structures with known inorganic applications or where relevant material properties are available.
• Theoretical inorganic structures
  Extracted from peer-reviewed journals
  Showing a low E(tot)
  Methods which lead to comparable experimental results

In particular, the database provides information on:

• structural data of pure elements, minerals, metals, and intermetallic compounds
• structural descriptors (Pearson symbol, ANX formula, Wyckoff sequences)
• bibliographic data and abstracts
• keywords on methods, properties and applications

• ICSD is the most complete database for inorganic crystal structures (including minerals, metals and alloys)
• Metalorganic structures
• Theoretical structures
• Acknowledged high data quality
• Known as reliable information in the community for more than 35 years
• Retrieval solutions for different needs (local installation, inhouse server, web-based)
• Consortia licenses (small consortia to nationwide licenses)

Indispensable tool for materials science

Reliable crystal structure data of high quality play an important part in optimizing the development of new materials which foster innovation in various areas. Crystallographic data can serve to explain and predict material properties. Therefore, material testing laboratories and researchers at universities and research institutions are dependent on evaluated crystal structure data. The huge amount of valuable information stored in crystal structure databases helps researchers in many ways, for example to provide input for a Rietveld refinement or data-mining parameters for structure prediction or optimization procedures.

Highly specialized functionalities

Because of highly specialized requests nowadays many tailor-made products are available. A lot of special databases cover particular interests like zeolite structures or mineral structures, but for general purposes overall collections of data are required. Crystal structure databases like ICSD contain much more information than just the obvious unit cells and atomic coordinates. For example, ICSD can be used to find similar structures by comparing certain features, like the space group or the ANX formula, that define different structure types.

High quality data

The ICSD data are of excellent quality. Only data which have passed thorough quality checks are included. Thousands of new structures are added each year, and the existing structures are regularly revised, corrected, and updated.

ICSD is an indispensable source of information for chemists, physicists, crystallographers, mineralogists and geologists teaching or doing research in the field of crystallography.

Experimental inorganic structures in ICSD can be

• either fully characterized where the atomic coordinates are determined and the composition is fully specified
• or the structure is published only with a structure type, so that the atomic coordinates and other parameters can be derived from existing data.

In addition to the full description of the published structure, the database contains additional information, such as the Wyckoff sequence, Pearson symbol, ANX formula, mineral name and group, structure type, and many more that can be useful in data mining.

Structure types have been introduced into ICSD in 2005 and were subsequently expanded during the last years. About 80% of the records are assigned to one of around 9,000 structure types. A new structure type in ICSD is only included if at least two compounds can be assigned to it. The two defining properties used to determine whether several crystal structures belong to the same structure type are that the structures are isopointal and isoconfigurational. As these properties are rather “unhandy” , some easily checkable properties like the ANX formula, Pearson symbol, Wyckoff sequence, c/a ratio etc. are used instead. A complete description of this procedure can be found in the article by Allmann and Hinek (see http://scripts.iucr.org/cgi-bin/paper?S0108767307038081).

Of special importance are remarks or comments that are generated when a new structure is entered. These remarks or comments can explain or at least highlight possible inconsistencies in the structure or describe actions taken during input to solve observed problems.

Keywords describe the applied experimental methods, properties and possible applications of the materials.

Recent advances in chemistry show that the distinction between inorganic and organic structures has become vague. This becomes more obvious in research areas on, for example, zeolites, catalysts, batteries, or gas storage systems. We now make the distinction on the basis of the point of view:

• If the focus of research is on the properties of the metal or non-C elements, then inorganic chemistry obviously is involved.
• If the metal-carbon bond or the inorganic partial structure is not in the focus of the work, e.g. the focus lies on the organic compound, organic chemistry is involved.

Using this distinction we have recently extended the scope of ICSD to metal-organic structures under certain conditions. We now include pure inorganic structures plus organometallic structures where material properties are available or where inorganic applications are known.
Biotechnological, medical or pharmaceutical contents are still not included.

We offer the following search functionalities:

• Group search for organometallic compounds
• Sum formula, linearized sum formula
• Compound name, name segments
• Elements, periodic groups, element count
• Text searches in abstracts
• Keywords for applications and material properties

ICSD is already extensively used in data mining and in computational chemistry. The traditional approach in materials research of first synthesizing new compounds and then checking their properties is rather time-consuming and quite expensive. One can already observe a strong tendency to shift materials research from the traditional synthesis-oriented approach to a more theory-oriented approach. Especially crystal structure predictions become more and more reliable. Therefore FIZ Karlsruhe has already started a collaboration to include theoretically calculated structures that are usually not experimentally determined into the ICSD. This allows for comparing calculated structures either with each other or directly with experimental data.

The major problem of theoretical structures is that a huge amount of calculations exists in a broad variety of quality. Therefore we developed a set of selection criteria to be able to provide only those structures which are interesting to use and analyze in a second step.
We have three major criteria for the selection:

1. the structure should be published in a peer-reviewed journal
2. the structures have a low E(tot) (close to the equilibrium structure)
3. we choose the method which delivers data that are closest to comparable experimental results

Theoretical crystal structures are clearly separated from experimental structures in the ICSD so that you can choose to search only experimental inorganic structures, or experimental metal-organic structures, only theoretical inorganic structures, or any combination of these three options. Furthermore we have categorized all theoretical structures by the following 13 methods used for the theoretical calculation listed below.

Additionally, each theoretical crystal structure entry is complemented with the following information:

• Code, with search algorithm (if present)
• Method/Functional
• Basis set information
• Details of calculation (Cutoff energy, K-point mash, etc).
• Standard comments: E(tot) ranking is missing in the paper. Additional structures are published in the manuscript.

In addition to these 13 theoretical methods, we provide further information about the comparison between the theoretical structure and experimental structures.

ICSD goes back to an initiative of Prof. Günter Bergerhoff in the late 1970s at the Institute for Inorganic Chemistry of the University of Bonn, Germany. FIZ Karlsruhe started to maintain the database in collaboration with the University of Bonn in 1985. In 1989 a joint venture between the Gmelin Institute, Germany, and FIZ Karlsruhe took over the responsibility for ICSD. From 1997 to 2017 ICSD was produced cooperatively by FIZ Karlsruhe, Germany, and the National Institute of Standards and Technology (NIST), U.S.A. Since 2017 ICSD has been solely produced by FIZ Karlsruhe.

In the past ICSD was mainly used to search for individual structures and as an aid for analysis.
In the meantime new aspects like development and optimization of materials, prediction of structures and materials properties and further development of methods came up.

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:
Zagorac, D., Müller, H., Ruehl, S., Zagorac, J. & Rehme, S., J. Appl. Cryst. 52 (2019), 918-925, “Recent developments in the Inorganic Crystal Structure Database: theoretical crystal structure data and related features”, https://doi.org/10.1107/S160057671900997X
Allmann, 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
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

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