Crystal Structure Database Research Articles

Enhancing Superconductor Critical Temperature Prediction: A Novel Machine Learning Approach Integrating Dopant Recognition.

Doping plays a crucial role in determining the critical temperature (Tc) of superconductors, yet accurately predicting its effects remains a significant challenge. Here, we introduce a novel doping descriptor that captures the complex influence of dopants on superconductivity. By integrating the doping descriptor with elemental and physical features within a Mixture of Experts (MoE) model, we achieve a remarkable R2 of 0.962 for Tc prediction, surpassing all published prediction models. Our approach successfully identifies optimal doping levels in the Bi2-xPbxSr2Ca2-yCuyOz system, with predictions closely aligning with experimental results. Leveraging this model, we screen compounds from the Inorganic Crystal Structure Database and employ a generative approach to explore new doped superconductors. This process reveals 40 promising candidates for high Tc superconductivity among existing and hypothetical doped materials. By explicitly accounting for doping effects, our method offers a powerful tool for guiding the experimental discovery of new superconductors, potentially accelerating progress in high-temperature superconductivity research and opening new avenues for material design.

Search for missing symmetry in the Inorganic Crystal Structure Database (ICSD).

An exhaustive search for missing symmetry was performed for 223 076 entries in the ICSD (2023-2 release). Approximately 0.65% of them can be described with higher symmetry than reported. Out of the identified noncentrosymmetric entries, ∼74% can be described by centrosymmetric space groups; this has implications for compatible physical properties. It is proposed that the information on the correct space group is included in the ICSD.

Deep learning for symmetry classification using sparse 3D electron density data for inorganic compounds

We report a novel deep learning (DL) method for classifying inorganic compounds using 3D electron density data. We transform Density Functional Theory (DFT)-derived CHGCAR files from the Materials Project (MP) and experimental data from the Inorganic Crystal Structure Database (ICSD) into point clouds and sparse tensors, optimized for use in DL models such as PointNet and Sparse 3D CNN. This approach effectively overcomes the limitations of handling the dense 3D data, a common challenge in DL. Contrasting with traditional 1D or 2D X-ray diffraction (XRD) patterns that necessitate complex reciprocal space analysis, our method utilizes 3D density data for direct interpretation in real lattice space. This shift significantly enhances classification accuracy, outperforming traditional XRD-driven DL methods. We achieve accuracies of 97.28%, 90.77%, and 90.10% for crystal system, extinction group, and space group classifications, respectively. Our 3D electron density-based DL approach not only showcases improved accuracy but also contributes a more intuitive and effective framework for materials discovery.

An electrochemical series for materials

The electrochemical series is a useful tool in electrochemistry, but its effectiveness in materials chemistry is limited by the fact that the standard electrochemical series is based on a relatively small set of reactions, many of which are measured in aqueous solutions. To address this problem, we have used machine learning to create an electrochemical series for inorganic materials from tens of thousands of entries in the Inorganic Crystal Structure Database. We demonstrate that this series is generally more consistent with oxidation states in solid-state materials than the series based on aqueous ions. The electrochemical series was constructed by developing and parameterizing a physical, human-interpretable model of oxidation states in materials. We show that this model enables the prediction of oxidation states from composition in a way that is more accurate than a state-of-the-art transformer-based neural network model. We present applications of our approach to structure prediction, materials discovery, and materials electrochemistry, and we discuss possible additional applications and areas for improvement. To facilitate the use of our approach, we introduce a freely available website and API.

Topology of Intermetallic Crystals: Classification, Uniformity, and Transitions.

We have analyzed the crystal structures of 7551 binary intermetallic compounds, which are deposited in the inorganic crystal structure database, using a combined geometrical-topological approach as implemented in our program package ToposPro. We represented each crystal structure with two models: (i) the topological model of periodic atomic net and (ii) the geometrical model of the Voronoi partition of crystal space. Within the former model, we have classified all intermetallics into 949 topological types, 20 of which cover 57% of the whole sample. Using our recently developed topological network model of reconstructive solid-state transformations, we have described possible mechanisms of displacive transitions between the most abundant topological types and revealed a key role of the body-centered lattice in these transitions. The Voronoi partition model enabled us to introduce the concept of uniformity of the overall structure and the environment of separate atoms; the uniformity is estimated by the second moment of inertia of atomic Voronoi polyhedra. We have shown that the structures with a low uniformity value either contain mistakes in the crystallographic data or have significantly directed interatomic bonds. The proposed uniformity criteria can serve for the estimation of the robustness of an intermetallic structure.

Genetic-algorithm-combined density functional theory calculations for investigating atomic properties of Si−Ge alloys

Compound semiconductors are expected to expand the possibilities of semiconductor applications. In particular, Ⅳ−Ⅳ compound semiconductors, such as silicon−germanium (Si−Ge) alloys, have received much attention over the past several decades. Although many scholars have conducted experiments on Si−Ge alloys, few scholars have conducted theoretical simulations on them. Thus, fundamental understanding of nanoscale phenomena in the Si−Ge alloys is still insufficient. To address this problem, we identified energetically stable structures of the Si−Ge alloys using density-functional-theory (DFT) first-principles calculations and genetic algorithm (GA) and evaluated various properties of the Si−Ge alloys. We confirmed that the formation energies of the stable structures identified by GA-combined DFT calculations were lower than those of other Si−Ge alloys registered in an existing crystal structure database. The lattice constants and electronic band gaps of these stable structures were evaluated using DFT calculations, and trends in their properties similar to those reported in previous experimental studies were verified. From the evaluation of coordination number according to the Boltzmann distribution, we found that homoelement bonds (Si–Si and Ge−Ge) are easier to form than heteroelement bonds (Si−Ge) in the stable structures of Si−Ge alloys at any composition.

A database of high-pressure crystal structures from hydrogen to lanthanum

This paper introduces the HEX (High-pressure Elemental Xstals) database, a complete database of the ground-state crystal structures of the first 57 elements of the periodic table, from H to La, at 0, 100, 200 and 300 GPa. HEX aims to provide a unified reference for high-pressure research, by compiling all available experimental information on elements at high pressure, and complementing it with the results of accurate evolutionary crystal structure prediction runs based on Density Functional Theory. Besides offering a much-needed reference, our work also serves as a benchmark of the accuracy of current ab-initio methods for crystal structure prediction. We find that, in 98% of the cases in which experimental information is available, ab-initio crystal structure prediction yields structures which either coincide or are degenerate in enthalpy to within 300 K with experimental ones. The main manuscript contains synthetic tables and figures, while the Crystallographic Information File (cif) for all structures can be downloaded from the related figshare online repository.

Accelerating ferroelectric materials discovery through high-throughput first-principles screening and experimental validation

We conducted high-throughput screening of ferroelectrics using first-principles calculations based on an existing crystal structure database. We focused on nonpolar structures with polar instability, to efficiently screen materials for their potential to undergo ferroelectric phase transitions from oxide materials in crystal structure databases. Our screening criteria included computational feasibility (excluding partial occupation), the absence of hazardous elements, and a maximum of 250 atoms in the conventional cell. Through this screening, we identified 47 ferroelectric candidates, 8 of which have already been reported as ferroelectrics. To validate our screening approach, we synthesized and evaluated several candidate materials with Dion–Jacobson-type structures, and measured their dielectric and ferroelectric properties. Although the ferroelectric behavior was not initially identified in these materials, our experiments confirmed their properties. Finally, we discovered a new ferroelectric material, CsCa2Nb3O10, which exhibited a ferroelectric phase transition at 28 K, clearly demonstrating the effectiveness of our screening strategy.

Two-Dimensional Transition Metal Phosphides As Cathode Additive in Robust Lithium-Sulfur Batteries.

The development of advanced cathode materials able to promote the sluggish redox kinetics of polysulfides is crucial to bringing lithium-sulfur batteries to the market. Herein, two electrode materials: namely, Zr2PS2 and Zr2PTe2, are identified through screening several hundred thousand compositions in the Inorganic Crystal Structure Database. First-principles calculations are performed on these two materials. These structures are similar to that of the classical MXenes. Concurrently, calculations show that Zr2PS2 and Zr2PTe2 possess high electrical conductivity, promote Li ion diffusion, and have excellent electrocatalytic activity for the Li-S reaction and particularly for the Li2S decomposition. Besides, the mechanisms behind the excellent predicted performance of Zr2PS2 and Zr2PTe2 are elucidated through electron localization function, charge density difference, and localized orbital locator. This work not only identifies two candidate sulfur cathode additives but may also serve as a reference for the identification of additional electrode materials in new generations of batteries, particularly in sulfur cathodes.

Major structural types in inorganic chemistry and mineralogy: New data

Research subject. Structural types with different stoichiometric correlations between chemical elements. Aim. To analyze the prevalence of structural types with different stoichiometric correlations between chemical elements, such as simple substances with binary compounds, triple compounds with stoichiometry ABX3, triple compounds with stoichiometry AB2X4. Key points. The analysis was conducted using the databases of inorganic compounds ICSD (Inorganic Crystal Structure Database) and PCD (Pearson’s Crystal Data). The number of entries with the most typical structural types for 2013 and 2023 are determined. Their classifications in various databases for different years are given. The ranks of structural types for minerals and inorganic compounds are analyzed. The minerals crystallized in all the considered structural types are indicated according to the 2023 ISCD data, sampling only by the number of minerals registered in IMA (International Mineralogical Association – Commission on New Minerals, Nomenclature and Classification) for March 2023. The Russian names of minerals are presented in accordance with the database WWW-MINCRIST for the minerals crystallizing in all the structural types under consideration. Conclusions. The most probable causes for the realization of each stoichiometric correlation in various structural types are determined. The prevalence of certain structural types among inorganic compounds and minerals, as well as the underlying reasons, are discussed based on the principles of crystal chemistry.

Halogen Bond via an Electrophilic π-Hole on Halogen in Molecules: Does It Exist?

This study reveals a new non-covalent interaction called a π-hole halogen bond, which is directional and potentially non-linear compared to its sister analog (σ-hole halogen bond). A π-hole is shown here to be observed on the surface of halogen in halogenated molecules, which can be tempered to display the aptness to form a π-hole halogen bond with a series of electron density-rich sites (Lewis bases) hosted individually by 32 other partner molecules. The [MP2/aug-cc-pVTZ] level characteristics of the π-hole halogen bonds in 33 binary complexes obtained from the charge density approaches (quantum theory of intramolecular atoms, molecular electrostatic surface potential, independent gradient model (IGM-δginter)), intermolecular geometries and energies, and second-order hyperconjugative charge transfer analyses are discussed, which are similar to other non-covalent interactions. That a π-hole can be observed on halogen in halogenated molecules is substantiated by experimentally reported crystals documented in the Cambridge Crystal Structure Database. The importance of the π-hole halogen bond in the design and growth of chemical systems in synthetic chemistry, crystallography, and crystal engineering is yet to be fully explicated.

Comparative Conformational Analysis of Acyclic Sugar Alcohols Ribitol, Xylitol and d-Arabitol by Solution NMR and Molecular Dynamics Simulations.

Ribitol (C5H12O5) is an acyclic sugar alcohol that was recently identified in O-mannose glycan on mammalian α-dystroglycan. The conformation and dynamics of acyclic sugar alcohols such as ribitol are dependent on the stereochemistry of the hydroxyl groups; however, the dynamics are not fully understood. To gain insights into the conformation and dynamics of sugar alcohols, we carried out comparative analyses of ribitol, d-arabitol and xylitol by a crystal structure database search, solution NMR analysis and molecular dynamics (MD) simulations. The crystal structures of the sugar alcohols showed a limited number of conformations, suggesting that only certain stable conformations are prevalent among all possible conformations. The three-bond scholar coupling constants and exchange rates of hydroxyl protons were measured to obtain information on the backbone torsion angle and possible hydrogen bonding of each hydroxyl group. The 100 ns MD simulations indicate that the ribitol backbone has frequent conformational transitions with torsion angles between 180∘ and ±60∘, while d-arabitol and xylitol showed fewer conformational transitions. Taking our experimental and computational data together, it can be concluded that ribitol is more flexible than d-arabitol or xylitol, and the flexibility is at least in part defined by the configuration of the OH groups, which may form intramolecular hydrogen bonds.

ClusterFinder: a fast tool to find cluster structures from pair distribution function data.

A novel automated high-throughput screening approach, ClusterFinder, is reported for finding candidate structures for atomic pair distribution function (PDF) structural refinements. Finding starting models for PDF refinements is notoriously difficult when the PDF originates from nanoclusters or small nanoparticles. The reported ClusterFinder algorithm can screen 104 to 105 candidate structures from structural databases such as the Inorganic Crystal Structure Database (ICSD) in minutes, using the crystal structures as templates in which it looks for atomic clusters that result in a PDF similar to the target measured PDF. The algorithm returns a rank-ordered list of clusters for further assessment by the user. The algorithm has performed well for simulated and measured PDFs of metal-oxido clusters such as Keggin clusters. This is therefore a powerful approach to finding structural cluster candidates in a modelling campaign for PDFs of nanoparticles and nanoclusters.

Understanding extended homometry based on complementary crystallographic orbit sets.

Extended homometry is a phenomenon in which distinct structures have the same X-ray diffraction (XRD) intensities, which may lead to incorrect results of structural analysis based on XRD methods. It is proposed and proved herein that half of a crystallographic orbit has the same powder X-ray diffraction intensity as its complementary set; three more theorems are deduced. These results are conducive to understanding the formation of extended homometric structures. Also analyzed are some reported or potential homometric or weakly homometric structures in the Inorganic Crystal Structure Database to confirm the theorems. This work presents a quick approach to analyze and construct extended homometric structures based on crystallographic orbits.

Chemical Rules for Stacked Kagome and Honeycomb Topological Semimetals.

The chemical rules for predicting and understanding topological states in stacked kagome and honeycomb lattices are studied in both analytical and numerical ways. Starting with a minimal five-band tight-binding model, all the topological states are sorted into five groups, which are determined by the interlayer and intralayer hopping parameters. Combined with the model, an algorithm is designed to obtain a series of experimentally synthesized topological semimetals with kagome and honeycomb layers, i.e., IAMX family (IA = Alkali metal element, M = Rare earth metal element, X = Carbon group element), in the inorganic crystal structure database. A follow-up high-throughput calculation shows that IAMX family materials are all nodal-line semimetals and they will be Weyl semimetals after taking spin-orbit coupling into consideration. To have further insights into the topology of the IAMX family, a detailed chemical rule analysis is carried out on the high-throughput calculations, including the lattice constants of the structure, intralayer and interlayer couplings, bond strengths, electronegativity, and so on, which are consistent with the tight-binding model. This study provides a way to discover and modulate topological properties in stacked kagome and honeycomb crystals and offers candidates for studying topology-related properties like topological superconductors and axioninsulators.

A database of computed Raman spectra of inorganic compounds with accurate hybrid functionals

Raman spectroscopy is widely applied in identifying local structures in materials, but the interpretation of Raman spectra is non-trivial. An accurate computational database of reference spectra calculated with a consistent level of theory can significantly aid in interpreting measured Raman spectra. Here, we present a database of Raman spectra of inorganic compounds calculated with accurate hybrid functionals in density functional theory. Raman spectra were obtained by calculating dynamical matrices and polarizability tensors for structures from the Inorganic Crystal Structure Database. The calculated Raman spectra and other phonon properties (e.g., infrared spectra) are stored in a MongoDB database publicly shared through a web application. We assess the accuracy of our Raman calculations by statistically comparing ~80 calculated spectra with an existing experimental Raman database. To date, the database contains 161 compounds and is continuously growing as we add more materials computed with our automated workflow.

Candidate ferroelectrics via ab initio high-throughput screening of polar materials

Ferroelectrics are a class of polar and switchable functional materials with diverse applications, from microelectronics to energy conversion. Computational searches for new ferroelectric materials have been constrained by accurate prediction of the polarization and switchability with electric field, properties that, in principle, require a comparison with a nonpolar phase whose atomic-scale unit cell is continuously deformable from the polar ground state. For most polar materials, such a higher-symmetry nonpolar phase does not exist or is unknown. Here, we introduce a general high-throughput workflow that screens polar materials as potential ferroelectrics. We demonstrate our workflow on 1978 polar structures in the Materials Project database, for which we automatically generate a nonpolar reference structure using pseudosymmetries, and then compute the polarization difference and energy barrier between polar and nonpolar phases, comparing the predicted values to known ferroelectrics. Focusing on a subset of 182 potential ferroelectrics, we implement a systematic ranking strategy that prioritizes candidates with large polarization and small polar-nonpolar energy differences. To assess stability and synthesizability, we combine information including the computed formation energy above the convex hull, the Inorganic Crystal Structure Database id number, a previously reported machine learning-based synthesizability score, and ab initio phonon band structures. To distinguish between previously reported ferroelectrics, materials known for alternative applications, and lesser-known materials, we combine this ranking with a survey of the existing literature on these candidates through Google Scholar and Scopus databases, revealing ~130 promising materials uninvestigated as ferroelectric. Our workflow and large-scale high-throughput screening lays the groundwork for the discovery of novel ferroelectrics, revealing numerous candidates materials for future experimental and theoretical endeavors.

Advancing Bioactivity Prediction through Molecular Docking and Self-Attention.

Bioactivity refers to the ability of a substance to induce biological effects within living systems, often describing the influence of molecules, drugs, or chemicals on organisms. In drug discovery, predicting bioactivity streamlines early-stage candidate screening by swiftly identifying potential active molecules. The popular deep learning methods in bioactivity prediction primarily model the ligand structure-bioactivity relationship under the premise of Quantitative Structure-Activity Relationship (QSAR). However, bioactivity is determined by multiple factors, including not only the ligand structure but also drug-target interactions, signaling pathways, reaction environments, pharmacokinetic properties, and species differences. Our study first integrates drug-target interactions into bioactivity prediction using protein-ligand complex data from molecular docking. We devise a Drug-Target Interaction Graph Neural Network (DTIGN), infusing interatomic forces into intermolecular graphs. DTIGN employs multi-head self-attention to identify native-like binding pockets and poses within molecular docking results. To validate the fidelity of the self-attention mechanism, we gather ground truth data from crystal structure databases. Subsequently, we employ these limited native structures to refine bioactivity prediction via semi-supervised learning. For this study, we establish a unique benchmark dataset for evaluating bioactivity prediction models in the context of protein-ligand complexes, showcasing the superior performance of our method (with an average improvement of 27.03%) through comparison with 9 leading deep learning-based bioactivity prediction methods.

A novel class of multivalent ionic conductors with the La3CuSiS7 structure type: results of stepwise ICSD screening.

The results of high-throughput screening of the inorganic crystal structure database for new promising Ca2+-, Mg2+-, Zn2+- and Al3+-ion conducting ternary and quaternary sulfides, selenides, and tellurides are presented (∼1500 compounds). A geometrical-topological approach based on the Voronoi partition was initially used and yielded 104 compounds, which were unknown as conductors with possible cation migration. All compounds were passed through the bond valence site energy analysis to determine the migration energy Em. Furthermore, we established the logarithmic dependencies of Em on the geometrical parameters of the migration pathways. As a result, 16 out of 104 structures were filtered out as promising conductors. Finally, density functional theory simulations yielded the 11 most prospective compounds with Em < 1.0 eV. Among them, we found a novel class of ionic conductors with the La3CuSiS7 structure, for which ab initio molecular dynamic calculations were performed, revealing diffusion coefficients of ∼10-7 cm2 s-1 and ionic conductivity of ∼10-2 S cm-1 at 300 K.

Synthesis and properties of anhydrous rare-earth phosphates, monazite and xenotime: a review.

The synthesis methods, crystal structures, and properties of anhydrous monazite and xenotime (REPO4) crystalline materials are summarized within this review. For both monazite and xenotime, currently available Inorganic Crystal Structure Database data were used to study the effects of incorporating different RE cations on the unit cell parameters, cell volumes, densities, and bond lengths. Domains of monazite-type and xenotime-type structures and other AXO4 compounds (A = RE; X = P, As, V) are discussed with respect to cation sizes. Reported chemical and radiation durabilities are summarized. Different synthesis conditions and chemicals used for single crystals and polycrystalline powders, as well as first-principles calculations of the structures and thermophysical properties of these minerals are also provided.