A crystallographic database is a database specifically designed to store information about the structure of molecules and crystals.
Crystals are solids having, in all three dimensions of space, a regularly repeating arrangement of atoms , ions , or molecules.
They are characterized by symmetry , morphology , and directionally dependent physical properties. A crystal structure describes the arrangement of atoms, ions, or molecules in a crystal. Molecules need to crystallize into solids so that their regularly repeating arrangements can be taken advantage of in X-ray , neutron , and electron diffraction based crystallography.
Crystal structures of crystalline material are typically determined from X-ray or neutron single-crystal diffraction data and stored in crystal structure databases.
They are routinely identified by comparing reflection intensities and lattice spacings from X-ray powder diffraction data with entries in powder-diffraction fingerprinting databases. Crystal structures of nanometer sized crystalline samples can be determined via structure factor amplitude information from single-crystal electron diffraction data or structure factor amplitude and phase angle information from Fourier transforms of HRTEM images of crystallites.
They are stored in crystal structure databases specializing in nanocrystals and can be identified by comparing zone axis subsets in lattice-fringe fingerprint plots with entries in a lattice-fringe fingerprinting database. Crystallographic databases differ in access and usage rights and offer varying degrees of search and analysis capacity.
Many provide structure visualization capabilities. They can be browser based or installed locally. Newer versions are built on the relational database model and support the Crystallographic Information File CIF as a universal data exchange format. Crystallographic data are primarily extracted from published scientific articles and supplementary material.
Newer versions of crystallographic databases are built on the relational database model, which enables efficient cross-referencing of tables. Cross-referencing serves to derive additional data or enhance the search capacity of the database. Data exchange among crystallographic databases, structure visualization software, and structure refinement programs has been facilitated by the emergence of the Crystallographic Information File CIF format.
The CIF format is the standard file format for the exchange and archiving of crystallographic data. The increasing automation of the crystal structure determination process has resulted in ever higher publishing rates of new crystal structures and, consequentially, new publishing models.
Minimalistic articles contain only crystal structure tables, structure images, and, possibly, abstract-like structure description. They tend to be published in author-financed or subsidized open-access journals. More elaborate contributions may go to traditional subscriber-financed journals. Hybrid journals, on the other hand, embed individual author-financed open-access articles among subscriber-financed ones. Crystal structure data in CIF format are linked to scientific articles as supplementary material.
In recent years, many publishers of crystallographic journals have come to interpret CIFs as formatted versions of open data , i.
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As of , more than , crystal structures had been published and stored in crystal structure databases. The publishing rate has reached more than 50, crystal structures per year.
These numbers refer to published and republished crystal structures from experimental data. Crystal structures are republished owing to corrections for symmetry errors, improvements of lattice and atomic parameters, and differences in diffraction technique or experimental conditions.
As of , there are about 1,, molecule and crystal structures known and published, approximately half of them in open access. Crystal structures are typically categorized as minerals , metals - alloys ,  inorganics ,  organics ,  nucleic acids ,  and biological macromolecules. Minerals are a subset of mostly inorganic compounds. Organic compounds and biological macromolecules are separated according to molecular size.
Organic salts, organometallics , and metalloproteins tend to be attributed to organics or biological macromolecules, respectively. Nucleic acids are a subset of biological macromolecules. Comprehensiveness can refer to the number of entries in a database.
On those terms, a crystal structure database can be regarded as comprehensive, if it contains a collection of all re- published crystal structures in the category of interest and is updated frequently. Searching for structures in such a database can replace more time-consuming scanning of the open literature.
Access to crystal structure databases differs widely.
Materials Science and Engineering: An Introduction
It can be divided into reading and writing access. Reading access rights search, download affect the number and range of users.
Independent of comprehensiveness, open-access crystal structure databases have spawned open-source software projects, such as search-analysis tools, visualization software, and derivative databases. Scientific progress has been slowed down by restricting access or usage rights as well as limiting comprehensiveness or data integrity.
Restricted access or usage rights are commonly associated with commercial crystal structure databases. Lack of comprehensiveness or data integrity, on the other hand, are associated with some of the open-access crystal structure databases other than the Crystallography Open Database COD ,   and is "macromolecular open-access counterpart", the world wide Protein Database.
Apart from that, several crystal structure databases are freely available for primarily educational purposes, in particular mineralogical databases and educational offshoots of the COD. Crystallographic databases can specialize in crystal structures, crystal phase identification, crystallization ,  crystal morphology, or various physical properties.
More integrative databases combine several categories of compounds or specializations. Search capacities of crystallographic databases differ widely. Basic functionality comprises search by keywords, physical properties, and chemical elements. Of particular importance is search by compound name and lattice parameters.
Very useful are search options that allow the use of wildcard characters and logical connectives in search strings. If supported, the scope of the search can be constrained by the exclusion of certain chemical elements.
More sophisticated algorithms depend on the material type covered. Organic compounds might be searched for on the basis of certain molecular fragments. Inorganic compounds , on the other hand, might be of interest with regard to a certain type of coordination geometry.
Search algorithms used for a more complex analysis of physical properties, e.
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Modern versions of crystallographic databases are based on the relational database model. Web-based databases typically process the search algorithm on the server interpreting supported scripting elements, while desktop-based databases run locally installed and usually precompiled search engines. Crystalline material may be divided into single crystals , twin crystals , polycrystals , and crystal powder. In a single crystal, the arrangement of atoms , ions , or molecules is defined by a single crystal structure in one orientation.
Twin crystals, on the other hand, consist of single-crystalline twin domains , which are aligned by twin laws and separated by domain walls.
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Polycrystals are made of a large number of small single crystals, or crystallites , held together by thin layers of amorphous solid.
Crystal powder is obtained by grinding crystals, resulting in powder particles, made up of one or more crystallites. Both polycrystals and crystal powder consist of many crystallites with varying orientation. Crystal phases are defined as regions with the same crystal structure, irrespective of orientation or twinning. Single and twinned crystalline specimens therefore constitute individual crystal phases.
Polycrystalline or crystal powder samples may consist of more than one crystal phase. Such a phase comprises all the crystallites in the sample with the same crystal structure. Crystal phases can be identified by successfully matching suitable crystallographic parameters with their counterparts in database entries.
Prior knowledge of the chemical composition of the crystal phase can be used to reduce the number of database entries to a small selection of candidate structures and thus simplify the crystal phase identification process considerably. Applying standard diffraction techniques to crystal powders or polycrystals is tantamount to collapsing the 3D reciprocal space , as obtained via single-crystal diffraction, onto a 1D axis. The resulting partial-to-total overlap of symmetry-independent reflections renders the structure determination process more difficult, if not impossible.
Reflection positions and intensities of known crystal phases, mostly from X-ray diffraction data, are stored, as d - I data pairs, in the Powder Diffraction File PDF database. Search-match algorithms compare selected test reflections of an unknown crystal phase with entries in the database.
X-ray powder diffraction fingerprinting has become the standard tool for the identification of single or multiple crystal phases and is widely used in such fields as metallurgy , mineralogy , forensic science , archeology , condensed matter physics , and the biological and pharmaceutical sciences. Powder diffraction patterns of very small single crystals, or crystallites , are subject to size-dependent peak broadening, which, below a certain size, renders powder diffraction fingerprinting useless.
In this case, peak resolution is only possible in 3D reciprocal space , i. Fourier transforms of HRTEM images and electron diffraction patterns both supply information about the projected reciprocal lattice geometry for a certain crystal orientation, where the projection axis coincides with the optical axis of the microscope. The vertical axis is defined as acute angle between Fourier transformed lattice fringes or electron diffraction spots. A 2D data point is defined by the length of a reciprocal lattice vector and its acute angle with another reciprocal lattice vector.
A suitable search-match algorithm using LFFPs, therefore, tries to find matching zone axis subsets in the database. It is, essentially, a variant of a lattice matching algorithm. In the case of electron diffraction patterns, structure factor amplitudes can be used, in a later step, to further discern among a selection of candidate structures so-called 'structure factor fingerprinting'. Structure factor amplitudes from electron diffraction data are far less reliable than their counterparts from X-ray single-crystal and powder diffraction data.
Existing precession electron diffraction techniques greatly improve the quality of structure factor amplitudes, increase their number and, thus, make structure factor amplitude information much more useful for the fingerprinting process.
Fourier transforms of HRTEM images, on the other hand, supply information not only about the projected reciprocal lattice geometry and structure factor amplitudes, but also structure factor phase angles. After crystallographic image processing,  structure factor phase angles are far more reliable than structure factor amplitudes. Further discernment of candidate structures is then mainly based on structure factor phase angles and, to a lesser extent, structure factor amplitudes so-called 'structure factor fingerprinting'.
The Generalized Steno Law  states that the interfacial angles between identical faces of any single crystal of the same material are, by nature, restricted to the same value.
This shall ensure that the correct indexing of the crystal faces is obtained for any single crystal.
It is in many cases possible to derive the ratios of the crystal axes for crystals with low symmetry from optical goniometry with high accuracy and precision and to identify a crystalline material on their basis alone employing databases such as 'Crystal Data'. The specimen goniometer of a TEM is thereby employed analogously to the goniometer head of an optical goniometer.
The optical axis of the TEM is then analogous to the reference direction of an optical goniometer. While in optical goniometry net-plane normals reciprocal lattice vectors need to be successively aligned parallel to the reference direction of an optical goniometer in order to derive measurements of interfacial angles, the corresponding alignment needs to be done for zone axes direct lattice vector in transmission electron goniometry.