Crystal Structure Database Research Articles

All topological bands of all nonmagnetic stoichiometric materials.

Topological quantum chemistry and symmetry-based indicators have facilitated large-scale searches for materials with topological properties at the Fermi energy (EF). We report the implementation of a publicly accessible catalog of stable and fragile topology in all of the bands both at and away from EF in the 96,196 processable entries in the Inorganic Crystal Structure Database. Our calculations, which represent the completion of the symmetry-indicated band topology of known nonmagnetic materials, have enabled the discovery of repeat-topological and supertopological materials, including rhombohedral bismuth and Bi2Mg3. We find that 52.65% of all materials are topological at EF, roughly two-thirds of bands across all materials exhibit symmetry-indicated stable topology, and 87.99% of all materials contain at least one stable or fragile topological band.

Crystal structure prediction with machine learning-based element substitution

The prediction of energetically stable crystal structures formed by a given chemical composition is a central problem in solid-state physics. In principle, the crystalline state of assembled atoms can be determined by optimizing the energy surface, which in turn can be evaluated using first-principles calculations. However, performing the iterative gradient descent on the potential energy surface using first-principles calculations is prohibitively expensive for complex systems, such as those with many atoms per unit cell. Here, we present a unique methodology for crystal structure prediction (CSP) that relies on a machine learning algorithm called metric learning. It is shown that a binary classifier, trained on a large number of already identified crystal structures, can determine the isomorphism of crystal structures formed by two given chemical compositions with an accuracy of approximately 96.4%. For a given query composition with an unknown crystal structure, the model is used to automatically select from a crystal structure database a set of template crystals with nearly identical stable structures to which element substitution is to be applied. Apart from the local relaxation calculation of the identified templates, the proposed method does not use ab initio calculations. The potential of this substitution-based CSP is demonstrated for a wide variety of crystal systems.

Efficient near-infrared phosphors discovered by parametrizing the Eu(II) 5d-to-4f energy gap

<h2>Summary</h2> Inorganic materials with rare-earth activators (e.g., Ce, Eu) exhibit broad 5<i>d</i>-to-4<i>f</i> emission spectra characterized by a strong host material dependency. Despite extensive research, the development of an efficient and near-infrared (NIR) 5<i>d</i>-to-4<i>f</i> emission remains elusive. Herein, we introduce key descriptors of the Eu(II)-host interactions and predict the in-crystal 5<i>d</i>-to-4<i>f</i> energy gap with a root-mean-square error of ca. 0.03 eV (7.0 nm). By incorporating this luminescence predictor into a high-throughput screening of 223 nitride materials in the Inorganic Crystal Structure Database, we identify and experimentally validate (Sr,Ba)₃Li₄Si₂N₆:Eu(II) with NIR emissions of λ_em = 800 ∼ 830 nm and high quantum efficiencies (QEs) of 30% ∼ 40%, leading to an NIR light power ∼3× superior to prevailing NIR emitters. The ultralong λ_em and high QE stem from a coordinated energy transfer and an optimized electronic delocalization around Eu(II). This work provides a cost-efficient computational approach for discovering phosphors with desired emissions.

Machine-learning correction to density-functional crystal structure optimization

Density functional theory is routinely applied to predict crystal structures. The most common exchange-correlation functionals used to this end are the Perdew–Burke–Ernzerhof (PBE) approximation and its variant PBEsol. We investigate the performance of these functionals for the prediction of lattice parameters and show how to enhance their accuracy using machine learning. Our data set is constituted by experimental crystal structures of the Inorganic Crystal Structure Database matched with PBE-optimized structures stored in the materials project database. We complement these data with PBEsol calculations. We demonstrate that the accuracy and precision of PBE/PBEsol volume predictions can be noticeably improved a posteriori by employing simple, explainable machine learning models. These models can improve PBE unit cell volumes to match the accuracy of PBEsol calculations, and reduce the error of the latter with respect to experiment by 35 percent. Further, the error of PBE lattice constants is reduced by a factor of 3–5. A further benefit of our approach is the implicit correction of finite temperature effects without performing phonon calculations.Impact statementKnowledge about the crystal structure of solids is essential for describing their elastic and electronic properties. In particular, their accurate prediction is essential to predict the electronic properties of not-yet-synthesized materials. Lattice parameters are most commonly calculated by density functional theory using the Perdew–Burke–Ernzerhof (PBE) approximation and its variant PBEsol as exchange-correlation functional. They are successful in describing materials properties but do, however, not always achieve the desired accuracy in comparison with experiments. We propose a computationally efficient scheme based on interpretable machine learning to optimize crystal structures. We demonstrate that the accuracy of PBE- and PBEsol-structures can be, therewith, enhanced noticeably. In particular, the PBE unit cells, available in materials databases, can be improved to the level of the more accurate PBEsol calculations and the error of the latter with respect to the experiment can be reduced by 35 percent. An additional advantage of our scheme is the implicit inclusion of finite temperature corrections, which makes expensive phonon calculations unnecessary.Graphical abstract

Structure and motifs of iron oxides from 1 to 3 TPa

Iron oxides are fundamental components of planet-forming materials. Understanding the Fe-O system's behavior and properties under high pressure can help us identify many new phases and states possible in exoplanetary interiors, especially terrestrial ones. Using the adaptive genetic algorithm (AGA), we investigate the structure of iron oxides for a wide range of stoichiometries ($0.25\leq x_O \leq 0.8$) at 1, 2, and 3 TPa. Five unreported ground-state structures with Fe$_2$O, FeO, Fe$_3$O$_5$, FeO$_2$, and FeO$_4$ compositions are identified. The calculated density of states (DOS) suggests that, except for FeO$_4$, all phases are metallic, but their carrier densities decrease with increasing pressure and oxygen content. The cluster alignment analysis of stable and metastable phases shows that several motifs may co-exist in a structure of iron oxides with low O content. In contrast, most iron oxides with high O content adopt a simple BCC motif at TPa pressures. Our results provide a crystal structure database of iron oxides for modeling and understanding the interiors of exoplanets.

The Stibium Bond or the Antimony-Centered Pnictogen Bond: The Covalently Bound Antimony Atom in Molecular Entities in Crystal Lattices as a Pnictogen Bond Donor.

A stibium bond, i.e., a non-covalent interaction formed by covalently or coordinately bound antimony, occurs in chemical systems when there is evidence of a net attractive interaction between the electrophilic region associated with an antimony atom and a nucleophile in another, or the same molecular entity. This is a pnictogen bond and are likely formed by the elements of the pnictogen family, Group 15, of the periodic table, and is an inter- or intra-molecular non-covalent interaction. This overview describes a set of illustrative crystal systems that were stabilized (at least partially) by means of stibium bonds, together with other non-covalent interactions (such as hydrogen bonds and halogen bonds), retrieved from either the Cambridge Structure Database (CSD) or the Inorganic Crystal Structure Database (ICSD). We demonstrate that these databases contain hundreds of crystal structures of various dimensions in which covalently or coordinately bound antimony atoms in molecular entities feature positive sites that productively interact with various Lewis bases containing O, N, F, Cl, Br, and I atoms in the same or different molecular entities, leading to the formation of stibium bonds, and hence, being partially responsible for the stability of the crystals. The geometric features, pro-molecular charge density isosurface topologies, and extrema of the molecular electrostatic potential model were collectively examined in some instances to illustrate the presence of Sb-centered pnictogen bonding in the representative crystal systems considered.

Screening Nitrides with High Debye Temperatures as Nonlinear Optical Materials

Nitrides, in which P in pnictides is substituted by N, are proposed as nonlinear optical (NLO) materials. Herein, 34 asymmetric nitrides collected in the Inorganic Crystal Structure Database (ICSD) are evaluated and predicted by first-principles calculations. As a result, three Si-nitrides possessing wide band gaps and large NLO coefficients are screened out: BeSiN2 (6.66 eV, d24 = −1.09 pm/V), LiSi2N3 (6.51 eV, d15 = −1.38 pm/V), and LaSi3N5 (4.73 eV, d14 = −0.98 pm/V). In addition, LiGaS2-like structure MoZn3N4 is a promising infrared NLO material with a large NLO coefficient d24 = −16.70 pm/V, which is 1.2 times that of the infrared benchmark AgGaS2 (d36 = 13.4 pm/V); meanwhile, its band gap of 3.05 eV is better than that of AgGaS2 (2.73 eV). Moreover, MoZn3N4 with a higher Debye temperature of 795.5 K, which suggests higher sound speeds and longer phonon lifetimes, thus should exhibit higher thermal conductivity. These nitrides represent a new exploration family of NLO materials.

The Pfizer Crystal Structure Database: An essential tool for structure‐based design at Pfizer

Pfizer's Crystal Structure Database (CSDB) is a key enabling technology that allows scientists on structure-based projects rapid access to Pfizer's vast library of in-house crystal structures, as well as a significant number of structures imported from the Protein Data Bank. In addition to capturing basic information such as the asymmetric unit coordinates, reflection data, and the like, CSDB employs a variety of automated methods to first ensure a standard level of annotations and error checking, and then to add significant value for design teams by processing the structures through a sequence of algorithms that prepares the structures for use in modeling. The structures are made available, both as the original asymmetric unit as submitted, as well as the final prepared structures, through REST-based web services that are consumed by several client desktop applications. The structures can be searched by keyword, sequence, submission date, ligand substructure and similarity search, and other common queries.

First-Principles Study of Three-Dimensional Electrides Containing One-Dimensional [Ba3N]3+ Chains.

Electrides, a unique type of compound where electrons act as anions, have a high electron mobility and a low work function, which makes them promising for applications in electronic devices and high-performance catalysts. The discovery of novel electrides and the expansion of the electride family have great significance for their promising applications. Herein, we reported four three-dimensional (3D) electrides by coupling crystal structure database searches and first-principles electronic structure analysis. Subnitrides (Ba3N, LiBa3N, NaBa3N, and Na5Ba3N) containing one-dimensional (1D) [Ba3N]3+ chains are identified as 3D electrides for the first time. The anionic electrons are confined in the 3D interstitial space of Ba3N, LiBa3N, NaBa3N, and Na5Ba3N. Interestingly, with the increase of Na content, the excess electrons of Na5Ba3N play two roles of metallic bonding and anionic electrons. Therefore, the subnitrides containing 1D [Ba3N]3+ chains can be regarded as a new family of 3D electrides, where anionic electrons reside in the 3D interstitial spaces and provide a conduction path. These materials not only are experimentally synthesizable 3D electrides but also are promising to be exfoliated into advanced 1D nanowire materials. Furthermore, our work suggests a discovery strategy of novel electrides based on one parent framework like [Ba3N]3+ chains, which would accelerate the mining of electrides from the crystal structure database.

Catalogue of flat-band stoichiometric materials.

Topological electronic flattened bands near or at the Fermi level are a promising route towards unconventional superconductivity and correlated insulating states. However, the related experiments are mostly limited to engineered materials, such as moiré systems1-3. Here we present a catalogue of the naturally occuring three-dimensional stoichiometric materials with flat bands around the Fermi level. We consider 55,206 materials from the Inorganic Crystal Structure Database catalogued using the Topological Quantum Chemistry website4,5, which provides their structural parameters, space group, band structure, density of states and topological characterization. We combine several direct signatures and properties of band flatness with a high-throughput analysis of all crystal structures. In particular, we identify materials hosting line-graph or bipartite sublattices-in either two or three dimensions-that probably lead to flat bands. From this trove of information, we create the Materials Flatband Database website, a powerful search engine for future theoretical and experimental studies. We use the database to extract a curated list of 2,379 high-quality flat-band materials, from which we identify 345 promising candidates that potentially host flat bands with charge centres that are not strongly localized on the atomic sites. We showcase five representative materials and provide a theoretical explanation for the origin of their flat bands close to the Fermi energy using the S-matrix method introduced in a parallel work6.

The Nitrogen Bond, or the Nitrogen-Centered Pnictogen Bond: The Covalently Bound Nitrogen Atom in Molecular Entities and Crystals as a Pnictogen Bond Donor

The nitrogen bond in chemical systems occurs when there is evidence of a net attractive interaction between the electrophilic region associated with a covalently or coordinately bound nitrogen atom in a molecular entity and a nucleophile in another, or the same molecular entity. It is the first member of the family of pnictogen bonds formed by the first atom of the pnictogen family, Group 15, of the periodic table, and is an inter- or intra-molecular non-covalent interaction. In this featured review, we present several illustrative crystal structures deposited in the Cambridge Structure Database (CSD) and the Inorganic Crystal Structure Databases (ICSD) to demonstrate that imide nitrogen is not the only instance where nitrogen can act as an electrophilic agent. Analysis of a set of carefully chosen illustrative crystal systems shows that a covalently bound nitrogen atom in a variety of molecular entities features a σ-hole or even a π-hole, and these have the ability to sustain attractive engagements with negative sites to form inter- and/or intramolecular interactions that drive, or assist, the formation of a crystalline phase.

Modulating Conformational Preferences by Allylic Strain toward Improved Physical Properties and Binding Interactions.

The preference of the axial over the equatorial orientation of 2-substitutent for both phenyl-1-piperidines and N-acylpiperidines is studied at the M06-2X level of theory. For phenyl-1-piperidines, the axial 2-substituent is modestly favored over the equatorial one. In contrast, the pseudoallylic strain in N-acylpiperidines dictates the axial orientation of 2-substituent with a ΔG up to −3.2 kcal/mol. The calculations agree well with the statistics from both the Cambridge Structural Database of small-molecule organic crystal structures and the Protein Data Bank. The equilibrium between the twist-boat and chair conformations for N-acylpiperidines with a 2-substituent was further investigated. The twist-boat conformation is found to be around 1.5 kcal/mol less favorable. Finally, the three-dimensionality in shape resulting from minimization of the pseudoallylic strain is characterized, and its implication in protein–ligand interactions is briefly reviewed.

The effect of alumina addition on the synthesis of zeolite from fly ash by hydrothermal method

ZSM-5 and analcime were successfully synthesized from coal fly ash using hydrothermal method in alkaline solution. The fly ash used was taken from the Paiton PLTU, one of the largest steam power plants in Indonesia. The fly ash was purified using sulphuric acid for 30 minutes and it was found that it has 17.7 wt.% of Si content and 2.79 wt.% of Al content. The zeolite synthesis was carried out in a hydrothermal reactor at 160 oC and 3 bar for 48 hours both with and without alumina addition. Subsequently, the zeolite produced was then calcined and characterized using X-ray diffraction (XRD) analysis. The results showed that ZSM-5 crystals were obtained from the hydrothermal process without alumina addition. On the other hand, the addition of alumina has a large impact on the hydrothermal process, which formed analcime crystals as the final product. Furthermore, the ZSM-5 and analcime produced have great correspondence to ICSD (Inorganic Crystal Structure Database) standards (no. 61010 for ZSM-5 and no. 34877 for analcime).

The Phosphorus Bond, or the Phosphorus-Centered Pnictogen Bond: The Covalently Bound Phosphorus Atom in Molecular Entities and Crystals as a Pnictogen Bond Donor.

The phosphorus bond in chemical systems, which is an inter- or intramolecular noncovalent interaction, occurs when there is evidence of a net attractive interaction between an electrophilic region associated with a covalently or coordinately bonded phosphorus atom in a molecular entity and a nucleophile in another, or the same, molecular entity. It is the second member of the family of pnictogen bonds, formed by the second member of the pnictogen family of the periodic table. In this overview, we provide the reader with a snapshot of the nature, and possible occurrences, of phosphorus-centered pnictogen bonding in illustrative chemical crystal systems drawn from the ICSD (Inorganic Crystal Structure Database) and CSD (Cambridge Structural Database) databases, some of which date back to the latter part of the last century. The illustrative systems discussed are expected to assist as a guide to researchers in rationalizing phosphorus-centered pnictogen bonding in the rational design of molecular complexes, crystals, and materials and their subsequent characterization.

Chemical Composition Data‐Driven Machine‐Learning Prediction for Phase Stability and Materials Properties of Inorganic Crystalline Solids

Materials informatics has attracted significant attention toward the efficient discovery and development of new functional materials. Machine‐learning regression techniques have often been used to establish a link between properties. In this respect, precise and rational regression is contingent on the choice of descriptors used to represent both composition and structural information. The usage of structure‐derived descriptors restricts the prediction range to the registered materials in the crystal structure database owing to the structural information requirements. Conversely, machine‐learning regression based only on compositional descriptors is free from this restriction, despite the fact that the prediction performance may diminish. Herein, their prediction performance is improved using compositional descriptors with histograms, and detailed surveys are performed on their ability to extrapolate. The proposed model achieves a high prediction accuracy based on the area under the receiver operating characteristic (ROC) curve (specifically, AUC &gt; 0.9).

XERUS: An Open‐Source Tool for Quick XRD Phase Identification and Refinement Automation

AbstractAnalysis of X‐ray diffraction patterns is one of the keystones of materials science and materials research. With the advancement of data‐driven methods for materials design, candidate materials can be quickly screened for the study of a desired physical property. Efficient methods to automatically analyze and identify phases present in a given pattern are paramount for the success of this new paradigm. To aid this process, the open‐source python package Xray Estimation and Refinement Using Similarity (XERUS) for semi‐automatic/automatic phase identification is presented. XERUS takes advantage of open crystal structure databases, not relying on proprietary databases, to obtain crystal structures on the fly, being then chemical space agnostic. By wrapping around GSASII scriptable, it can automatically simulate patterns and calculate similarity measures used for phase identification. This approach is simple and quick but also applicable to multiphase identification, by coupling the similarity calculations with quick refinements followed by an iterative peak removal process. XERUS is shown in action in four different experimental datasets, and also it is benchmarked against a recently proposed deep learning method for a mixture dataset covering the Li‐Mn‐O‐F chemical space.

On the combinatorics of crystal structures: number of Wyckoff sequences of given length.

A formula for the calculation of the number of Wyckoff sequences of a given length is presented, based on the combinatorics of multisets with finite multiplicities and a generating function approach, assuming a certain space-group type and taking into account the number of non-fixed and fixed Wyckoff positions, respectively. The formula is applied to the 44 distinguishable combinatorial types of the 230 space-group types. A comparison is made between the calculated frequencies of occurrence of Wyckoff sequences of given space-group type and length and the observed ones for actual crystal structures, as retrieved from the Pearson's Crystal Data Crystal Structure Database for Inorganic Compounds.

Theoretical Study of the Geometry of Dibenzoazepine Analogues.

The geometry of dibenzoazepine analogues—typical multifunctional drugs—was investigated to find the geometrical parameters sensitive to the substitution of the central seven-membered ring. Exploration of the crystal structure database (CSD) shows that the geometrical parameter sensitive to the substitution of the carbon atom distance of the central ring not included in the aromatic rings to the plane through the carbon atoms common for the central ring and the aromatic side rings. Presence of the double bond in the central ring was reflected in its partial aromaticity expressed by the HOMED parameter. Some derivatives of 5H-dibenzo[b,f]azepine with flat conformation of the central ring are characterized by mobility of the electron density comparable to the mobility in the aromatic side rings. Influence of the surrounding on the investigated compounds was confirmed by comparison of the optimized molecules and the molecules in the crystal state where the packing forces can influence the molecular geometry.

A general method for searching for homometric structures.

The discovery of the homometry of X-ray diffraction dates back to the 1930s and shows the ambiguity in structure characterization purely from diffraction technologies. Since then, there has only been limited discussion on this topic with reference to several real structures and an efficient method for finding homometric structures for a known three-dimensional structure is still lacking today. In this work, a method was developed to create a set of structures with the same interatomic distances as an original structure, which can then be identified as powder X-ray diffraction or single-crystal X-ray diffraction homometric structures. This method was applied to the Inorganic Crystal Structure Database (ICSD) and homometric structures for 37 reported structures were found. Also it was found that homometric structures are encountered frequently in solving crystal structures when building initial models with only heavy atoms, which indicates that special caution is needed in interpreting XRD data with heavy-atom methods.

High-Throughput Computational Screening for Bipolar Magnetic Semiconductors.

Searching ferromagnetic semiconductor materials with electrically controllable spin polarization is a long-term challenge for spintronics. Bipolar magnetic semiconductors (BMS), with valence and conduction band edges fully spin polarized in different spin directions, show great promise in this aspect because the carrier spin polarization direction can be easily tuned by voltage gate. Here, we propose a standard high-throughput computational screening scheme for searching BMS materials. The application of this scheme to the Materials Project database gives 11 intrinsic BMS materials (1 experimental and 10 theoretical) from nearly ~40000 structures. Among them, a room-temperature BMS Li2V3TeO8 (mp-771246) is discovered with a Curie temperature of 478 K. Moreover, the BMS feature can be maintained well when cutting the bulk Li2V3TeO8 into (001) nanofilms for realistic applications. This work provides a feasible solution for discovering novel intrinsic BMS materials from various crystal structure databases, paving the way for realizing electric-field controlled spintronics devices.