Crystallographic database
A crystallographic database is a database specifically designed to store information about the structure of
Crystal structures of crystalline material are typically determined from
Crystal structures of nanometer sized crystalline samples can be determined via
Overview
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.[1] It was adopted by the International Union of Crystallography (IUCr), who also provides full specifications of the format.[2] It is supported by all major crystallographic databases.
The increasing automation of the
Crystal structure data in CIF format are linked to scientific articles as supplementary material. CIFs may be accessible directly from the publisher's website, crystallographic databases, or both. In recent years, many publishers of crystallographic journals have come to interpret CIFs as formatted versions of open data, i.e. representing non-copyrightable facts, and therefore tend to make them freely available online, independent of the accessibility status of linked scientific articles.
Trends
As of 2008, more than 700,000
Crystal structures are typically categorized as
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. It can be divided into reading and writing access. Reading access rights (search, download) affect the number and range of users. Restricted reading access is often coupled with restricted usage rights. Writing access rights (upload, edit, delete), on the other hand, determine the number and range of contributors to the database. Restricted writing access is often coupled with high data integrity.
In terms of user numbers and daily access rates, comprehensive and thoroughly vetted
Search
Search capacities of
More sophisticated algorithms depend on the material type covered.
Modern versions of crystallographic databases are based on the
Crystal phase identification
Polycrystals are made of a large number of small single crystals, or
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.
Powder diffraction fingerprinting (1D)
Applying standard
Search-match algorithms compare selected test reflections of an unknown crystal phase with entries in the database. Intensity-driven algorithms utilize the three most intense lines (so-called ‘Hanawalt search’), while d-spacing-driven algorithms are based on the eight to ten largest d-spacings (so-called ‘Fink search’).[17]
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
Lattice-fringe fingerprinting (2D)
High-Resolution Transmission Electron Microscopy (HRTEM) provides images and diffraction patterns of nanometer sized crystallites. 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.
Projected lattice geometries can be represented by so-called ‘lattice-fringe fingerprint plots’ (LFFPs), also called angular covariance plots.[18] The horizontal axis of such a plot is given in reciprocal lattice length and is limited by the point resolution 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. Sets of 2D data points that obey Weiss's zone law are subsets of the entirety of data points in an LFFP. 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.[19]
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.[20]
Morphological fingerprinting (3D)
The Generalized
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'.
Since Steno's Law can be further generalized for a single crystal of any material to include the angles between either all identically indexed net planes (i.e. vectors of the reciprocal lattice, also known as 'potential reflections in diffraction experiments') or all identically indexed lattice directions (i.e. vectors of the direct lattice, also known as zone axes), opportunities exist for morphological fingerprinting of nanocrystals in the transmission electron microscope (TEM) by means of transmission electron goniometry.[30]
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. (Note that such alignments are by their nature quite trivial for nanocrystals in a TEM after the microscope has been aligned by standard procedures.)
Since transmission electron goniometry is based on
Lattice matching (3D)
Arbitrarily defined unit cells can be transformed to a standard setting and, from there, further reduced to a primitive smallest cell. Sophisticated algorithms compare such reduced cells with corresponding database entries. More powerful algorithms also consider derivative super- and subcells. The lattice-matching process can be further sped up by precalculating and storing reduced cells for all entries. The algorithm searches for matches within a certain range of the lattice parameters. More accurate lattice parameters allow a narrower range and, thus, a better match.[31]
Lattice matching is useful in identifying crystal phases in the early stages of single-crystal diffraction experiments and, thus, avoiding unnecessary full data collection and structure determination procedures for already known crystal structures. The method is particularly important for single-crystalline samples that need to be preserved. If, on the other hand, some or all of the crystalline sample material can be ground, powder diffraction fingerprinting is usually the better option for crystal phase identification, provided that the peak resolution is good enough. However, lattice matching algorithms are still better at treating derivative super- and subcells.
Visualization
Newer versions of
Crystal structures
The
The visualization of a crystal structure can be reduced to the arrangement of atoms, ions, or molecules in the unit cell, with or without cell outlines. Structure elements extending beyond single unit cells, such as isolated
The space group of a crystal is a mathematical description of the symmetry inherent in the structure. The motif of the crystal structure is given by the asymmetric unit, a minimal subset of the unit cell contents. The unit cell contents can be fully reconstructed via the symmetry operations of the space group on the asymmetric unit. Visualization interfaces usually allow for switching between asymmetric unit and full structure representations.
Since
Crystal structure visualization can be integrated into a
Currently, web-integrated crystal structure visualization is based on
Morphology and physical properties
Crystal morphology or physical property data can be stored in specialized databases or added to more comprehensive crystal structure databases. The Crystal Morphology Database (CMD) is an example for a web-based crystal morphology database with integrated visualization capabilities.
See also
References
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- ^ US patent 8131481, Peter Moeck, "Database Supported Nanocrystal Structure Identification by Lattice-Fringe Fingerprinting with Structure Factor Extraction"
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- ^ Moeck Peter, Čertík Ondřej, Upreti Girish, Seipel Björn, Harvey Morgan, Garrick William, Fraundorf Philip (2006). "Crystal Structure Visualizations in three Dimensions with Support from the Open Access Nano-Crystallography Database". Journal of Materials Education. 28 (1): 83–90.
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External links
Crystal structures
- American Mineralogist Crystal Structure Database (AMCSD) (contents: crystal structures of minerals, access: free, size: large)
- Cambridge Structural Database (CSD) (contents: crystal structures of organics and metal-organics, access: restricted, size: very large)
- Crystallography Open Database (COD) (contents: crystal structures of organics, metalorganics, minerals, inorganics, metals, alloys, and intermetallics, access: free, size: very large)
- COD+ (Web Interface for COD) (contents: crystal structures of organics, metalorganics, minerals, inorganics, metals, alloys, and intermetallics, access: free, size: very large)
- Database of Zeolite Structures (contents: crystal structures of zeolites, access: free, size: small)
- Incommensurate Structures Database (contents: incommensurate structures, access: free, size: small)
- Inorganic Crystal Structure Database (ICSD) (contents: crystal structures of minerals and inorganics, access: restricted, size: large)
- MaterialsProject Database (contents: crystal structures of inorganic compounds, access: free, size: large)
- Materials Platform for Data Science (MPDS) or PAULING FILE (contents: critically evaluated crystal structures, as well as physical properties and phase diagrams, from the world scientific literature, access: partially free, size: very large)
- MaterialsWeb Database (contents: crystal structures of inorganic 2D materials and bulk compounds, access: free, size: large)
- Metals Structure Database (CRYSTMET) (contents: crystal structures of metals, alloys, and intermetallics, access: restricted, size: large)
- Mineralogy Database (contents: crystal structures of minerals, access: free, size: medium)
- MinCryst (contents: crystal structures of minerals, access: free, size: medium)
- NIST Structural Database NIST Structural Database (contents: crystal structures of metals, alloys, and intermetallics, access: restricted, size: large)
- NIST Surface Structure Database (contents: surface and interface structures, access: restricted, size: small-medium)
- Nucleic Acid Database (contents: crystal and molecular structures of nucleic acids, access: free, size: medium)
- Pearson's Crystal Data (contents: crystal structures of inorganics, minerals, salts, oxides, hydrides, metals, alloys, and intermetallics, access: restricted, size: very large)
- Worldwide Protein Data Bank (PDB) (contents: crystal and molecular structures of biological macromolecules, access: free, size: very large)
- Wiki Crystallography Database (WCD) (contents: crystal structures of organics, metalorganics, minerals, inorganics, metals, alloys, and intermetallics, access: free, size: medium)
Crystal phase identification
- Match! (method: powder diffraction fingerprinting)
- NIST Crystal Data (method: lattice matching)
- Powder Diffraction File (PDF) (method: powder diffraction fingerprinting)
Specialized databases
- Educational Subset of the Crystallography Open Database (EDU-COD) (specialization: crystal and molecule structures for college education, access: free, size: medium)
- Biological Macromolecule Crystallization Database (BMCD) (specialization: crystallization of biological macromolecules, access: free, size: medium)
- Crystal Morphology Database (CMD) (specialization: morphology of crystals, access: free, size: very small)
- Database of Hypothetical Structures Archived 2016-01-24 at the Wayback Machine (specialization: predicted zeolite-like crystal structures, access: free, size: large)
- Database of Zeolite Structures (specialization: crystal structures of zeolites, access: free, size: small)
- Hypothetical MOFs Database Archived 2019-02-19 at the Wayback Machine (specialization: predicted metal-organic framework crystal structures, access: free, size: large)
- Incommensurate Structures Database (specialization: incommensurate structures, access: free, size: small)
- Marseille Protein Crystallization Database (MPCD) (specialization: crystallization of biological macromolecules, access: free, size: medium)
- MOFomics (specialization: pore structures of metal-organic frameworks, access: free, size: medium)
- Nano-Crystallography Database (NCD) (specialization: crystal structures of nanometer sized crystallites, access: free, size: small)
- NIST Surface Structure Database (specialization: surface and interface structures, access: restricted, size: small-medium)
- Predicted Crystallography Open Database (PCOD) (spezialization: predicted crystal structures of organics, metal-organics, metals, alloys, intermetallics, and inorganics, access: free, size: very large)
- Theoretical Crystallography Open Database (TCOD) (spezialization: crystal structures of organics, metal-organics, metals, alloys, intermetallics, and inorganics that were refined or predicted from density functional theory with some experimental input, access: free, size: small)
- ZEOMICS (specialization: pore structures of zeolites, access: free, size: small)