Inorganic Crystal Structure Database Research Articles

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.

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.

Local Atomic Configurations in Intermetallic Crystals: Beyond the First Coordination Shell.

We have used a combined geometrical-topological approach to analyze 21,697 intermetallic crystal structures stored in the Inorganic Crystal Structure Database. Following a geometrical scheme of close packing of balls, we have considered the three most typical polyhedral atomic environments of the icosahedral, cuboctahedral, or twinned cuboctahedral shape as well as multi-shell (up to four shells) local atomic configurations (LACs) based on these cores in 10,657 unique crystal structure determinations. In total, half of intermetallic structures have been found to contain one of these configurations, with the icosahedral LACs being the most frequent. We have revealed that even a two-shell configuration strongly predetermines the overall connectivity (topological type) of an intermetallic crystal structure. The chemical and stoichiometric composition of the multi-shell LACs generally obeys the close-packing model: the number of atoms in the subsequent shells (Nk) varies around the value Nk = 10k2 + 2, which is valid for the same size atoms, to reach the densest packing for the kth shell. Deviations from the revealed regularities often indicate inconsistencies in the crystallographic information, unusual features of the structure, or the existence of more stable phases that can be used for the validation of experimental and modeling data.

High-throughput search for potential permanent magnet materials

High-performance permanent magnets, which have a wide range of applications in the information age, are characterized by significant magnetic anisotropy mainly affected by spin-orbit coupling (SOC). The authors perform a highly efficient search for permanent magnet materials in the inorganic crystal structure database by focusing on materials containing $3d$ transition elements with specific Wyckoff positions, where certain partially occupied orbital multiplets can significantly enhance the effect of SOC. According to common standards of permanent magnets, the authors propose five new permanent magnet candidates. They believe that these potential permanent magnet materials deserve further experimental study.

Shooting mid-infrared nonlinear optical materials with targeted balance performances in ternary d0-transition metal oxides through first-principles

Heavy metal oxides are emergent candidates for mid-infrared nonlinear optical (NLO) materials, which might achieve a targeted balance property between the idea band gaps and large NLO coefficients. However, the ternary d0-transition metal oxides as mid-infrared NLO materials have not been systematically investigated by the first-principles. Herein, taking ternary d0-transition metal oxides as a research subject, we collected 94 non-centrosymmetric compounds in the inorganic crystal structure database for screening excellent mid-infrared NLO crystals. Among them, seven compounds are highlighted: HT-La2Ti2O7, ZrMo2O8, Rb3V5O14, β-Tl3VO4, La2MoO6, WO2Cl2, and WOCl4. They have a good balance between the wide band gaps 3.01–4.33 eV and large NLO coefficients of 0.5–0.7 × AgGaS2 (d36 = 13.4 p.m./V), simultaneously possess moderate birefringence of 0.055–0.438 at 1064 nm. This study will provide a guideline for exploring new mid-infrared NLO materials with targeted balance performances in ternary d0-transition metal oxides.

Boron Substructures in Inorganic Borides: Network Topology and Free Space

We have analyzed polymeric boron substructures in all 1154 robust inorganic borides from the Inorganic Crystal Structure Database using topological and geometrical models as implemented in the ToposPro program package. We have classified all boron substructures into 63 topological types of atomic networks depending on the method of connection and discriminated them by periodicity into molecular, chain, layer, or framework motifs. We have revealed topological relations between boron motifs of different topologies and periodicities and found the boron substructures that can serve as templates for assembling motifs of a more complicated topology and/or a higher periodicity. We have applied the natural tiling approach to find all cages in a particular boron substructure and estimated the size of these cages with Voronoi polyhedra. The sizes of all metal atoms, which occur in the inorganic borides, have also been calculated in the same way. As a result, we have explained the composition of some borides and showed that the combined geometrical–topological approach helps one to estimate the feasibility of possible new boride structures and to refine the conditions for the synthesis or to prepare initial structural data for the subsequent predictive modeling.

Predicting Synthesizability using Machine Learning on Databases of Existing Inorganic Materials.

Defining the metric for synthesizability and predicting new compounds that can be experimentally realized in the realm of data-driven research is a pressing problem in contemporary materials science. The increasing computational power and advancements in machine learning (ML) algorithms provide a new avenue to solve the synthesizability challenge. In this work, using the Inorganic Crystal Structure Database (ICSD) and the Materials Project (MP) database, we represent crystal structures in Fourier-transformed crystal properties (FTCP) representation and use a deep learning model to predict synthesizability in the form of a synthesizability score (SC). Such an SC model, as a synthesizability filter for new materials, enables an efficient and accurate classification to identify promising material candidates. The SC prediction model achieved 82.6/80.6% (precision/recall) overall accuracy in predicting ternary crystal materials. We also trained the SC model by only considering compounds uploaded on the MP before 2015 as the training set and testing on multiple sets of materials uploaded after 2015. In the post-2019 test set, we obtain a high 88.60% true positive rate accuracy, coupled with 9.81% precision, indicating that newly added materials remain unexplored and have high synthesis potential. Further, we provide a list of 100 materials predicted to be synthesizable from this post-2019 dataset (highest SC) for future studies, and our SC model, as a validation filter, is beneficial for future material screening and discovery.

Solid electrolytes for Li-ion batteries via machine learning

In this work, machine learning (ML) techniques were employed to construct a predictive model that can be used to discover new solid state electrolytes (SE/SSE) for lithium ion batteries (LIBs). The model was built (with R2 = 0.97) based on a dataset constructed from previous works regarding ionic conductivity (IC) of solid electrolytes. After a suitable validation process, the ML-model was used to predict the IC of many compositions (∼30 K in Inorganic Crystal Structure Database (ICSD)). Interestingly, the predictions of this model, done on 145 compounds, were consistent with values of Li-phonon band center, which is used as an IC descriptor, this was then used to predict the IC vs temperature behavior of LiYS2 which is suggested as a promising SSE candidate in this work.

A database of experimentally measured lithium solid electrolyte conductivities evaluated with machine learning

The application of machine learning models to predict material properties is determined by the availability of high-quality data. We present an expert-curated dataset of lithium ion conductors and associated lithium ion conductivities measured by a.c. impedance spectroscopy. This dataset has 820 entries collected from 214 sources; entries contain a chemical composition, an expert-assigned structural label, and ionic conductivity at a specific temperature (from 5 to 873 °C). There are 403 unique chemical compositions with an associated ionic conductivity near room temperature (15–35 °C). The materials contained in this dataset are placed in the context of compounds reported in the Inorganic Crystal Structure Database with unsupervised machine learning and the Element Movers Distance. This dataset is used to train a CrabNet-based classifier to estimate whether a chemical composition has high or low ionic conductivity. This classifier is a practical tool to aid experimentalists in prioritizing candidates for further investigation as lithium ion conductors.