Inorganic 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.

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.

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.

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.

Transforming materials discovery for artificial photosynthesis: High-throughput screening of earth-abundant semiconductors

We present a highly efficient workflow for designing semiconductor structures with specific physical properties, which can be utilized for a range of applications, including photocatalytic water splitting. Our algorithm generates candidate structures composed of earth-abundant elements that exhibit optimal light-trapping, high efficiency in H2 and/or O2 production, and resistance to reduction and oxidation in aqueous media. To achieve this, we use an ionic translation model trained on the Inorganic Crystal Structure Database to predict over 30 000 undiscovered semiconductor compositions. These predictions are then screened for redox stability under hydrogen evolution reaction or oxygen evolution reaction conditions before generating thermodynamically stable crystal structures and calculating accurate bandgap values for the compounds. Our approach results in the identification of dozens of promising semiconductor candidates with ideal properties for artificial photosynthesis, offering significant advancement toward the conversion of sunlight into chemical fuels.

Ideal Vacuum-Based Efficient and High-Throughput Computational Screening of Type II Heterojunctions.

Heterojunctions featuring a type II band alignment play a crucial role in a wide range of devices, particularly in the realm of solar cells. However, the design of such heterojunctions with a specific type of band alignment poses a substantial challenge due to the immense number of potential combinations of bulk semiconductors and their relative orientations. In this study, we propose an efficient, high-throughput computational screening method tailored for heterojunctions. Our approach, using the ideal vacuum level as a reference energy, eliminates the need for explicit electronic structure calculations for junctions. Through this protocol, we identify 1041 type II heterojunctions out of 2692 structures constructed from 86 selected inorganic compounds with appropriate band gaps sourced from the Inorganic Crystal Structure Database. For potential application in solar cells, we assess these heterojunctions, and remarkably, 58 of them exhibit a power conversion efficiency (PCE) exceeding 15%, with 13 surpassing the 20% threshold. Test calculations with expensive interface models confirm the reliability of PCE predictions based on ideal vacuums. These predictions will be of benefit in assessing the material applicability for solar cell applications. Furthermore, the versatility of our proposed screening method extends beyond solar cells, making it a valuable theoretical design tool that can be applied to a wide range of heterojunction devices.

Stability of intermetallic compounds: Geometrical and topological aspects

We have applied a universal geometrical-topological approach to analyze 23507 robust crystal structures of intermetallic compounds, which are deposited in the Inorganic Crystal Structure Database. All the structures were represented as three-periodic nets and arranged into 2551 topological types each of which possesses a unique system of interatomic bonds. For all 104309 topologically different atoms in these structures their environments (local atomic configurations, LACs) were explored within the first and second coordination shells. In total, 5251 topologically distinct types of coordination polyhedra and 21450 types of two-shell LACs were distinguished and deposited into our interactive database system TopCryst. We have found that the local density of the nearest environment of an atom naturally depends on its coordination number (CN) with a maximum at CN = 9. It was shown that LACs at CN ≥ 9 follow the close packing model of hard balls with Slater's atomic radii. At lower CNs, covalent interactions make a significant contribution that results in a decrease of the local density. Resting upon the dependence between the local density and CN we have proposed criteria for the estimation of the geometric instability (disbalance) of both a separate LAC and the whole intermetallic structure. We have revealed that strong geometric disbalance can be caused by errors in the structural data, incorrect description of the crystal structure, non-metallic nature of the chemical bonding or metastability of the crystalline phase. Thus the instability criteria can serve as important indicators of the structure inconsistency that is especially useful for checking crystallographic information on intermetallics or validating the structural models predicted by theoretical methods.

The Influence of Polyvinyl Alcohol Porogen Addition on the Nanostructural Characteristics of Hydroxyapatite.

Hydroxyapatite (HA) is a porous material widely developed in various research fields because of its high biodegradability, biocompatibility, and low toxicity. In this research, HA was synthesized using a hydrothermal method with chicken eggshells as a calcium source and various concentrations of polyvinyl alcohol as a porogen (2.5%, 5.0%, and 7.5% by wt). The structure and morphology of HA were determined by X-ray diffraction (XRD) and scanning electron microscope (SEM), respectively. HA was obtained with varying concentrations of polyvinyl alcohol (PVA) porogen according to Inorganic Crystal Structure Database (ICSD) standard. Based on analysis using a refinement method, changes in unit cell parameters (cell volume and lattice strain) of HA synthesized using PVA porogen compared to the standard, the chi square (χ2) and index of R values were relatively low, validating the acceptable of the data. In addition, HA [Ca10(PO4)6(OH)2] with hexagonal structure and the P63/m space group was successfully obtained. Morphological analysis of HA by SEM found that HA has a spherical shape, and the porosity of HA increases with increasing concentrations of polyvinyl alcohol. The highest porosity was obtained with an addition of 5.0 wt% of PVA porogen (HAP3), reaching 69.53%.

A unified understanding of minimum lattice thermal conductivity

We propose a first-principles model of minimum lattice thermal conductivity ([Formula: see text]) based on a unified theoretical treatment of thermal transport in crystals and glasses. We apply this model to thousands of inorganic compounds and find a universal behavior of [Formula: see text] in crystals in the high-temperature limit: The isotropically averaged [Formula: see text] is independent of structural complexity and bounded within a range from ∼0.1 to ∼2.6 W/(m K), in striking contrast to the conventional phonon gas model which predicts no lower bound. We unveil the underlying physics by showing that for a given parent compound, [Formula: see text] is bounded from below by a value that is approximately insensitive to disorder, but the relative importance of different heat transport channels (phonon gas versus diffuson) depends strongly on the degree of disorder. Moreover, we propose that the diffuson-dominated [Formula: see text] in complex and disordered compounds might be effectively approximated by the phonon gas model for an ordered compound by averaging out disorder and applying phonon unfolding. With these insights, we further bridge the knowledge gap between our model and the well-known Cahill-Watson-Pohl (CWP) model, rationalizing the successes and limitations of the CWP model in the absence of heat transfer mediated by diffusons. Finally, we construct graph network and random forest machine learning models to extend our predictions to all compounds within the Inorganic Crystal Structure Database (ICSD), which were validated against thermoelectric materials possessing experimentally measured ultralow κL. Our work offers a unified understanding of [Formula: see text], which can guide the rational engineering of materials to achieve [Formula: see text].

Prediction of large magnetic moment materials with graph neural networks and random forests

Magnetic materials are crucial components of many technologies that could drive the ecological transition, including electric motors, wind turbine generators and magnetic refrigeration systems. Discovering materials with large magnetic moments is therefore an increasing priority. Here, using state-of-the-art machine learning methods, we scan the Inorganic Crystal Structure Database (ICSD) of hundreds of thousands of existing materials to find those that are ferromagnetic and have large magnetic moments. Crystal graph convolutional neural networks (CGCNN), materials graph network (MEGNet) and random forests are trained on the Materials Project database that contains the results of high-throughput DFT predictions. For random forests, we use a stochastic method to select nearly one hundred relevant descriptors based on chemical composition and crystal structure. This gives results that are comparable to those of neural networks. The comparison between these different machine learning approaches gives an estimate of the errors for our predictions on the ICSD database. Validating our final predictions by comparisons with available experimental data, we found 15 materials that are likely to have large magnetic moments and have not been yet studied experimentally.