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Guidance on Formulating Compressed Solids
Published in Sarfaraz K. Niazi, Handbook of Pharmaceutical Manufacturing Formulations, Third Edition, 2019
There appears to be a relationship between indentation hardness and the molecular structure of organic materials. However, a prerequisite for predicting indentation hardness is knowledge of the crystal structure. As a result, highly sophisticated computational methods and extensive crystallography libraries have recently become available to study this. For example, the Pfizer Research relies on the Cambridge Structural Database (CSD) (http://www.ccdc.cam.ac.uk/), the world repository of small molecule crystal structures. The CSD is the principal product of the Cambridge Crystallographic Data Centre (CCDC). It is the central focus of the CSD System, which also comprises software for database access, structure visualization and data analysis, and structural knowledge bases derived from the CSD. The CSD records bibliographic, chemical, and crystallographic information for organic molecules and metal–organic compounds whose 3D structures have been determined using X-ray diffraction or neutron diffraction. The CSD records results of single crystal studies and powder diffraction studies which yield 3D atomic coordinate data for at least all non-H atoms. In some cases, the CCDC is unable to obtain coordinates, and incomplete entries are archived to the CSD. The CSD is distributed as part of the CSD System, which includes software for search and information retrieval (ConQuest), structure visualization (Mercury), numerical analysis (Vista), and database creation (PreQuest). The CSD System also incorporates IsoStar, a knowledge base of intermolecular interactions that contains data derived from both the CSD and the Protein Data Bank (PDB). Some of the software listed here are available for free use.
Physical Methods for Characterizing Solids
Published in Elaine A. Moore, Lesley E. Smart, Solid State Chemistry, 2020
It is now standard academic practice to deposit final published structures and their data in a crystallographic database, of which there are several: the Cambridge Structural Database, CSD (for small organic and organometallic molecules); the Inorganic Crystal Structure Database, ICSD, the Crystallographic Open Database, COD; CRYSTMET for metals and alloys; the Protein Data Bank, PDB; and the Nucleic Acid Database, NDB. These databases check the deposited structure and its data through their own software to ensure internal consistency.
Prediction of hydrogen adsorption in nanoporous materials from the energy distribution of adsorption sites*
Published in Molecular Physics, 2019
Arun Gopalan, Benjamin J. Bucior, N. Scott Bobbitt, Randall Q. Snurr
As a complement to traditional materials design approaches, high-throughput computational screening [10] has recently emerged as a powerful method for identifying promising materials for a variety of applications. Databases that could be screened to find nanoporous materials for hydrogen storage include the Cambridge Structural Database (CSD) [11], the Computation-Ready Experimental (CoRE) Metal-Organic Framework database (refined from CSD) [12], the ideal silica zeolites from the International Zeolite Association (IZA) [13], a database of hypothetical MOFs (hMOFs) [14], the Predicted Crystallography Open Database (PCOD) [15], a set of energetically feasible, zeolite-like materials generated from a computational search through crystal space groups and possible unit cell sizes [16], hypothetical Covalent Organic Frameworks (hCOFs) [17], hypothetical Zeolitic Imidazolate Frameworks (hZIFs, where the Si-O bond in the hypothetical zeolites is replaced by the Zn-N bond) [18], and the hypothetical Porous Polymer Networks (hPPNs) [19]. These databases contain over 850,000 crystalline nanoporous materials.
Topological representations of crystal structures: generation, analysis and implementation in the TopCryst system
Published in Science and Technology of Advanced Materials: Methods, 2022
Alexander P. Shevchenko, Aleksandr A. Shabalin, Igor Yu. Karpukhin, Vladislav A. Blatov
The atomic structures of crystalline solids were determined by diffraction methods during more than a century. Although the diffraction experiment provides the data on the distribution of electron density, thus keeping the crystal space continuity, this information is usually lost in public access. The retained structural information, which is now collected in a number of electronic world-wide databases, such as Cambridge Structural Database (CSD) [1], Inorganic Crystal Structure Database (ICSD) [2], Crystallography Open Database (COD) [3] and Pearson’s Crystal Data (PCD) [4], describes only positions of maxima of electron density (atoms) and the structure symmetry, thus bearing only geometrical properties of the structure. When a chemist analyzes this information, he/she should again restore the structure connectivity, i.e. the bonds between atoms. Since besides the atom names, only geometrical information is available at this stage, the criteria for determining the bonds can also be only geometrical. One can distinguish two groups of such criteria: (i) distance criteria, which use interatomic distances or other parameters derived from them, such as atomic radii [5] or bond strengths [6], and (ii) polyhedron criteria, which rest upon Voronoi polyhedra [7]; the criteria from these groups can be combined. However, for a long time, there was no universal method for automated determination of atomic coordination numbers, and this problem hindered the application of machine methods to processing the crystallographic information. After determining the connectivity, the structural model can be transformed from a set of isolated atoms to a periodic net [8,9], which represents topological properties of a crystal structure. In this century, topological databases, Reticular Chemistry Structure Resource (RCSR) [10], Euclidean Patterns in Non-Euclidean Tilings (EPINET) [11] and TOPOS Topological Databases (TTD) [12], were developed, which gather periodic nets of a particular connectivity. The occurrences of the topologies in crystal structures are a subject of the Topological Types Observed (TTO) collection [12]. Next problem concerned recognition of structural units (molecules, ligands, clusters and tiles), analysis of their connection and the corresponding polymeric groups. Last, the topology of the periodic structural motifs should be determined and classified for establishing relations between crystal structures of different composition and complexity. All these problems taken together form a general challenge to the modern crystallochemical analysis: how to effectively use a huge amount of crystallographic information stored in the electronic databases. In our program package ToposPro [13], we proposed solutions of separate problems mentioned above, but there were no tool to perform the whole topological analysis in a fully automated way. In this paper, we present such tool, which unites the ToposPro algorithms in an easy-to-use Internet service.