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Structural DNA nanotechnology is an emerging field and is expected to be used for various applications in materials science. In this study, we designed a DNA tensegrity triangle to accommodate the bipyridine complexes with metal ions (Ni2+ and Fe2+) at the center of the space within the triangle. A metal–bipyridine-incorporated DNA tensegrity triangle was crystalized, and the presence of metals within it was confirmed through X-ray crystal structure analysis. A signal of the anomalous dispersion effect derived from metal was observed in the center of the DNA triangle. [ABSTRACT FROM AUTHOR]

All-insulator heterostructures with an emerging metallicity are at the forefront of material science, which typically contain at least one band insulator while it is not necessary to be. Here we show emergent phenomena in a series of all-correlated-insulator heterostructures that composed of insulating CaIrO3 and insulating La0.67Sr0.33MnO3. We observed an intriguing insulator-to-metal transition, that depends delicately on the thickness of the iridate component. The simultaneous enhancements of magnetization, electric conductivity, and magnetoresistance effect indicate a percolation-type nature of the insulator-to-metal transition, with the percolation threshold can be reached at an exceptionally low volume fraction of the iridate. Such a drastic transition is induced by an interfacial charge transfer, which interestingly alters the electronic and crystalline structures of the bulk region rather than the limited ultrathin interface. We further showcased the central role of effective correlation in modulating the insulator-to-metal transition, by demonstrating that the critical thickness of iridate for triggering the metallic state can be systematically reduced down to a single unit-cell layer. Insulator-metal transitions driven by electronic correlations in oxide heterostructures have been widely studied, usually involving a correlated insulator and a band insulator. Here the authors study all-correlated-insulator heterostructures and observe a thickness-dependent insulator-metal transition. [ABSTRACT FROM AUTHOR]

In recent years, notable progress in computational density functional theory (DFT) has facilitated the collection of extensive datasets in the field of materials science. Machine learning is a crucial technique for effectively processing and analyzing these large datasets and accelerating the creation of new compounds. In this work, we employ the Extreme Gradient Boosting (XGBoost) classification algorithm to predict the crystal structure of 381 halides and oxide perovskites using 78 features. We achieved a classification accuracy of 76.62% for these materials. Subsequently, we utilized the Random Forest and XGBoost algorithms to investigate a dataset comprising 761 perovskite materials from the material project database to predict the band gap energy (best accuracy = 84.78%, mean absolute error(MAE) = 0.410 eV, root mean square error (RMSE) = 0.594 eV) and formation energy (best accuracy = 94.81%, MAE = 0.083 eV/atom, RMSE = 0.157 eV/atom), all of these prediction results based on the chemical formulas. Notably, the feature importance analysis shows the significant influence of the number of d electrons in the d orbit of the B atom on both band gap (Eg) and formation energy (EF) properties. Finally, we applied our prediction model to identify stable perovskite solar cells, achieving an accuracy of 85.18%. These findings provide valuable guidelines for the discovery of stable perovskite solar cells and help researchers tailor the composition of perovskite materials to optimize their light-harvesting capabilities, leading to higher-power conversion efficiencies. [ABSTRACT FROM AUTHOR]

Deformcell is a python script designed to simplify and fasten routine procedure of preparing crystal structure files for calculating various mechanical properties in VASP program. The software allows users to prepare POSCAR files for calculations of different mechanical properties performing energy calculations of deformed crystallographic unit cells. It has been specially designed to allow users to automate unit cell deformation prior to energy calculations, making it useful both for scientific research and teaching computational chemistry applied to chemical materials sciences. [ABSTRACT FROM AUTHOR]

Skin, the largest organ of the human body, accounts for protecting against external injuries and pathogens. Despite possessing inherent self-regeneration capabilities, the repair of skin lesions is a complex and time-consuming process yet vital to preserving its critical physiological functions. The dominant treatment involves the application of a dressing to protect the wound, mitigate the risk of infection, and decrease the likelihood of secondary injuries. Pursuing solutions for accelerating wound healing has resulted in groundbreaking advancements in materials science, from hydrogels and hydrocolloids to foams and micro-/nanofibers. Noting the convenience and flexibility in design, nanofibers merit a high surface-area-to-volume ratio, controlled release of therapeutics, mimicking of the extracellular matrix, and excellent mechanical properties. Core-shell nanofibers bring even further prospects to the realm of wound dressings upon separate compartments with independent functionality, adapted release profiles of bioactive agents, and better moisture management. In this review, we highlight core-shell nanofibers for wound dressing applications featuring a survey on common materials and synthesis methods. Our discussion embodies the wound healing process, optimal wound dressing characteristics, the current organic and inorganic material repertoire for multifunctional core-shell nanofibers, and common techniques to fabricate proper coaxial structures. We also provide an overview of antibacterial nanomaterials with an emphasis on their crystalline structures, properties, and functions. We conclude with an outlook for the potential offered by core-shell nanofibers toward a more advanced design for effective wound healing. [ABSTRACT FROM AUTHOR]

Recent advancements in materials science have shed light on the potential of exploring hierarchical assemblies of molecules on surfaces, driven by both fundamental and applicative challenges. This field encompasses diverse areas including molecular storage, drug delivery, catalysis, and nanoscale chemical reactions. In this context, the utilization of nanotube templates (NTs) has emerged as promising platforms for achieving advanced one-dimensional (1D) molecular assemblies. NTs offer cylindrical, crystalline structures with high aspect ratios, capable of hosting molecules both externally and internally (Mol@NT). Furthermore, NTs possess a wide array of available diameters, providing tunability for tailored assembly. This review underscores recent breakthroughs in the field of Mol@NT. The first part focuses on the diverse panorama of structural properties in Mol@NT synthesized in the last decade. The advances in understanding encapsulation, adsorption, and ordering mechanisms are detailed. In a second part, the review highlights the physical interactions and photophysics properties of Mol@NT obtained by the confinement of molecules and nanotubes in the van der Waals distance regime. The last part of the review describes potential applicative fields of these 1D heterostructures, providing specific examples in photovoltaics, luminescent materials, and bio-imaging. A conclusion gathers current challenges and perspectives of the field to foster discussion in related communities. [ABSTRACT FROM AUTHOR]

High‐entropy materials (HEMs) represent a revolutionary class of materials that have garnered significant attention in the field of materials science due to their extraordinary properties in diverse fields of applications such as catalysis and electrochemistry. The past decade has witnessed a substantial increase in the study of these materials, exploring new synthesis routes and compositions. What began as the synthesis of high‐entropy alloys has expanded to encompass several classes of HEMs such as oxides, hydroxides, sulfides, nitrides, and carbides, among others. Several synthesis methods have been developed to produce these materials. This review therefore highlights the fundamental concepts of HEMs, including their core effects, with a major emphasis on their scalable synthesis routes. The advantages and drawbacks of these methods are also discussed. As HEMs transition from the lab to large‐scale production, there is a growing need for cost‐effective and scalable synthesis methods with high material yield suitable for a variety of applications like hydrogen storage, catalysis, batteries, supercapacitors, and fuel cells. Hence, this review serves as an introduction to scalable synthesis routes based on crystal structure, desired elements, synthesis times, and equipment costs. [ABSTRACT FROM AUTHOR]

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Rules that control the arrangement of chemical species within crystalline arrays of different symmetry and structural complexity are of fundamental importance in geoscience, material science, physics, and chemistry. Here, the volume of crystal phases is normalized by their ionic volume and an algebraic index that is based on their space-group and crystal site symmetries. In correlation with the number of chemical formula units Z, the normalized volumes exhibit upper and lower limits of possible structures. A bottleneck of narrowing limits occurs for Z around 80 to 100, but the field of allowed crystalline configurations widens above 100 due to a change in the slope of the lower limit. For small Z, the highest count of structures is closer to the upper limit, but at large Z, most materials assume structures close to the lower limit. In particular, for large Z, the normalized volume provides rather narrow constraints for the prediction of novel crystalline phases. In addition, an index of higher and lower complexity of crystalline phases is derived from the normalized volume and tested against key criteria. [ABSTRACT FROM AUTHOR]

Durable interest in developing a framework for the detailed structure of glassy materials has produced numerous structural descriptors that trade off between general applicability and interpretability. However, none approach the combination of simplicity and wide-ranging predictive power of the lattice-grain-defect framework for crystalline materials. Working from the hypothesis that the local atomic environments of a glassy material are constrained by enthalpy minimization to a low-dimensional manifold in atomic coordinate space, we develop a generalized distance function, the Gaussian Integral Inner Product (GIIP) distance, in connection with agglomerative clustering and diffusion maps, to parameterize that manifold. Applying this approach to a two-dimensional model crystal and a three-dimensional binary model metallic glass results in parameters interpretable as coordination number, composition, volumetric strain, and local symmetry. In particular, we show that a more slowly quenched glass has a higher degree of local tetrahedral symmetry at the expense of cyclic symmetry. While these descriptors require post-hoc interpretation, they minimize bias rooted in crystalline materials science and illuminate a range of structural trends that might otherwise be missed. The structure of crystalline materials plays a central role in materials science, but the disordered structure of metallic glass is difficult to characterize and describe. Here, the authors use diffusion maps on atomistic data to obtain general structural descriptors tied to atomic positions. [ABSTRACT FROM AUTHOR]

Benefited from the lower dimensionality compared to their 3D counterpart, 2D flat‐band systems provide cleaner lattice models, easier experimental verification, and higher tunability, which make the 2D van der Waals (vdW) system an ideal playground for exploring flat‐band physics as well as their potential applications. Given the vast amount of research in the field of flat bands, a simple and efficient approach to search for realistic vdW materials with flat bands is still missing. Here, a two‐tier framework to filter and diagnose high‐quality flat‐band vdW materials by combining high‐throughput first‐principles calculations and the proposed 2D flat‐band score criterion is presented. Based on systematic geometrical analysis, 861 potential monolayer vdW materials are initially obtained amounting to 187,093 structures as stored in the Inorganic Crystal Structure Database. By applying the 2D flat‐band score criterion, 229 flat‐band candidates are efficiently identified, among which a sub‐catalog of 74 materials with flat bands right next to the Fermi level is further provided to facilitate experimental verification. All these efforts to screen experimentally available flat‐band candidates will certainly motivate continuing exploration toward the realization of this class of special materials and their applications in material science. [ABSTRACT FROM AUTHOR]

The exploration of multiferroic materials and their interaction with light at the nanoscale presents a captivating frontier in materials science. Bismuth Ferrite (BiFeO3, BFO), a standout among these materials, exhibits room-temperature ferroelectric and antiferromagnetic behaviour and magnetoelectric coupling. Of particular interest is the phenomenon of photostriction, the light-induced deformation of crystal structures, which enhances the prospect for device functionality based on these materials. Understanding and harnessing multiferroic phenomena holds significant promise in various technological applications, from optoelectronics to energy storage. The orientation of the ferroelectric axis is an important design parameter for devices formed from multiferroic materials. Determining its orientation in the laboratory frame of reference usually requires knowing multiple wavevector transfer (Q-Vector) directions, which can be challenging to establish due to the need for extensive reciprocal-space searches. Our study demonstrates a method to identify the ferroelectric axis orientation using Bragg Coherent X-ray Diffraction Imaging (BCDI) measurements at a single Q-vector direction. This method involves applying photostriction-inducing laser illumination across various laser polarisations. Our findings reveal that photostriction primarily occurs as a surface phenomenon at the nanoscale. Moreover, a photo-induced crystal length change ranging from 30 to 60 nm was observed, consistent with earlier findings on bulk material. [ABSTRACT FROM AUTHOR]

Why are materials with specific characteristics more abundant than others? This is a fundamental question in materials science and one that is traditionally difficult to tackle, given the vastness of compositional and configurational space. We highlight here the anomalous abundance of inorganic compounds whose primitive unit cell contains a number of atoms that is a multiple of four. This occurrence—named here the rule of four—has to our knowledge not previously been reported or studied. Here, we first highlight the rule's existence, especially notable when restricting oneself to experimentally known compounds, and explore its possible relationship with established descriptors of crystal structures, from symmetries to energies. We then investigate this relative abundance by looking at structural descriptors, both of global (packing configurations) and local (the smooth overlap of atomic positions) nature. Contrary to intuition, the overabundance does not correlate with low-energy or high-symmetry structures; in fact, structures which obey the rule of four are characterized by low symmetries and loosely packed arrangements maximizing the free volume. We are able to correlate this abundance with local structural symmetries, and visualize the results using a hybrid supervised-unsupervised machine learning method. [ABSTRACT FROM AUTHOR]

Recently, dithienopyrazines have emerged as promising building blocks in the field of materials science, showcasing their potential as hole-transport materials in organic electronic devices. Herein, we report the synthesis of its heavier analogues, the diselenopyrazines, along with an analysis of their optoelectronic and structural properties. In the acquired crystal structures, interesting molecular packing motifs suitable for potential device fabrication were observed. Additionally, short contact interactions are present in one of the investigated molecules. [ABSTRACT FROM AUTHOR]

Hafnium carbide (HfC) is a refractory compound known for its exceptional mechanical, thermal, and electrical properties. This compound has gained significant attention in materials science and engineering due to its high melting point, extreme hardness, and excellent thermal stability. This study presents crystal structure prediction via energy landscape explorations of pristine hafnium carbide supplemented by data mining. Apart from the well-known equilibrium rock salt phase, we predict eight new polymorphs of HfC. The predicted HfC phases appear in the energy landscape with known structure types such as the WC type, NiAs type, 5-5 type, sphalerite (ZnS) type, TlI type, and CsCl type; in addition, we predict two new structure types denoted as ortho_HfC and HfC_polytype, respectively. Moreover, we have investigated the structural characteristics and mechanical properties of hafnium carbide at the DFT level of computation, which opens diverse applications in various technological domains. [ABSTRACT FROM AUTHOR]

Inherently poor lattice thermal conductivity (⁠ κ L ⁠) is highly desired for applications like thermoelectricity, thermal management in electronics, thermal barrier coatings and refractories. Recently, complex crystalline materials have drawn serious scientific attention because of various interesting underlying physical phenomena which explain the unique thermal properties. In this review, we have discussed various interesting concepts and their consequences leading to ultralow κ L in complex bulk chalcogenide minerals having multiple scattering channels for heat-carrying phonons. The primary focus of this review is on the Ag- and Cu-based large unit cell structures with low heat capacity and liquid-like superionic conduction of cations. The Ag/Cu sublattice of these materials that followed the phonon-liquid electron-crystal concept strongly reduces the transportation of phonons and enhances the scattering process. The presence of a large number of atoms in the unit cell results in low acoustic phonons cut-off frequency, robust acoustic–optical phonons scattering, poor sound velocity and strong crystal anharmonicity inside the crystalline lattice. [ABSTRACT FROM AUTHOR]

The structure and assembly properties of ferritin derived from aquatic products remain to be explored. Constructing diverse three-dimensional (3D) protein architectures with the same building blocks has important implications for nutrient delivery, medicine and materials science. Herein, ferritin from Asterias forbesii (AfFer) was prepared, and its crystal structure was resolved at 1.91 Å for the first time. Notably, different from the crystal structure of other reported ferritin, AfFer exhibited a BCT lattice arrangement in its crystals. Bioinspired by the crystal structure of AfFer, we described an effective approach for manufacturing 3D porous, crystalline nanoarchitectures by redesigning the shared protein interface involved in different 3D protein arrays. Based on this strategy, two 3D superlattices of body-centered tetragonal and simple cubicwere constructed with ferritin molecules as the building blocks. This study provided a potentially generalizable strategy for constructing different 3D protein-based crystalline biomaterials with the same building blocks. [ABSTRACT FROM AUTHOR]

Crystal structure prediction is a central problem of crystallography and materials science, which until mid-2000s was considered intractable. Several methods, based on either energy landscape exploration or, more commonly, global optimization, largely solved this problem and enabled fully non-empirical computational materials discovery. A major shortcoming is that, to avoid expensive calculations of the entropy, crystal structure prediction was done at zero Kelvin, reducing to the search for the global minimum of the enthalpy rather than the free energy. As a consequence, high-temperature phases (especially those which are not quenchable to zero temperature) could be missed. Here we develop an accurate and affordable solution, enabling crystal structure prediction at finite temperatures. Structure relaxation and fully anharmonic free energy calculations are done by molecular dynamics with a forcefield (which can be anything from a parametric forcefield for simpler cases to a trained on-the-fly machine learning interatomic potential), the errors of which are corrected using thermodynamic perturbation theory to yield accurate results with full ab initio accuracy. We illustrate this method by applications to metals (probing the P–T phase diagram of Al and Fe), a refractory covalent solid (WB), an Earth-forming silicate MgSiO3 (at pressures and temperatures of the Earth's lower mantle), and ceramic oxide HfO2. [ABSTRACT FROM AUTHOR]

Unconventional noncovalent interactions are ubiquitous in molecular crystals, supramolecular systems, and biomolecules, but their significance has often been overshadowed by stronger intermolecular interactions such as hydrogen bonding and π‐stacking. This study aims to emphasize the significance of nonclassical noncovalent interactions in crystal packing. The crystal structure of 1,1′‐(1‐chloro‐4‐methoxyphenyl)dibenzene was reported as a representative case. A network of weak interactions governs the arrangement of the molecules within the crystal, encompassing C−H⋅⋅⋅X hydrogen bonding (where X=O, Cl, or π), weak Cl⋅⋅⋅Cl symmetrical bonding (classified as Type I halogen–halogen bonding), and C−H...H−C homopolar dihydrogen contacts (dH...H=2.348 Å). The nature, energetics, and cooperativity of these interactions were evaluated using computational calculations based on dispersion‐corrected density functional theory (DFT/wB97X‐D/aug‐cc‐pVTZ). Our findings contribute to a comprehensive understanding of unconventional noncovalent interactions and their role in crystal packing. The methods and concepts employed are likely to be applicable to other molecular systems in biology, chemistry, and materials science. [ABSTRACT FROM AUTHOR]

Volumetric mass density and changes in crystal structure analysis of Sn-xBi alloy and Sn-xBi-yAl alloys have been conducted to enhance our understanding of these materials. The Sn-xBi alloy, with various compositions including Sn-0Bi, Sn-10Bi, Sn-30Bi, Sn-52Bi, and Sn-70Bi, and the Sn-xBi-yAl alloys, with compositions including Sn-52Bi-0.05Al, Sn-52Bi-0.11Al, Sn-52Bi-0.14Al, Sn-52Bi-0.19Al, and Sn-52Bi-0.25Al, were synthesized for this research. The selection of Sn-xBi-yAl compositions was based on the optimal composition of Sn-xBi alloys, specifically Sn-52Bi, which possesses the lowest melting point at 142.8°C. The determination of melting points was carried out using the Differential Scanning Calorimetry (DSC) method. Crystal structure characterization was accomplished using X-ray diffraction (XRD) techniques, with subsequent analysis performed through the Generalized Structure Analysis System (GSAS) method. These investigations are pivotal as they provide insights into the material properties and structural changes in SnxBi and Sn-xBi-yAl alloys, which have significant implications in various industrial applications and technological advancements. The results of this research are expected to contribute to the optimization of these alloy systems, leading to improved performance and potential innovations in materials science and engineering. [ABSTRACT FROM AUTHOR]

This document is a foreword to a collection of research articles published by African researchers in crystallography journals. The collection celebrates the founding of the African Crystallographic Association (AfCA) and highlights the contributions of African scientists to the field. The document acknowledges the challenges faced by African researchers, such as limited infrastructure and research funding, but also emphasizes the progress and developments in crystallography in Africa. It discusses collaborations, educational initiatives, and the growth of crystallography as a science in Africa. The document concludes by stating that African crystallographers are producing excellent science and that crystallography in Africa has come of age. [Extracted from the article]

Finding an optimal match between two different crystal structures underpins many important materials science problems, including describing solid-solid phase transitions and developing models for interface and grain boundary structures. In this work, we formulate the matching of crystals as an optimization problem where the goal is to find the alignment and the atom-to-atom map that minimize a given cost function such as the Euclidean distance between the atoms. We construct an algorithm that directly solves this problem for large finite portions of the crystals and retrieves the periodicity of the match subsequently. We demonstrate its capacity to describe transformation pathways between known polymorphs and to reproduce experimentally realized structures of semi-coherent interfaces. Additionally, from our findings, we define a rigorous metric for measuring distances between crystal structures that can be used to properly quantify their geometric (Euclidean) closeness. [ABSTRACT FROM AUTHOR]

Dynamic metal nanoclusters have garnered widespread attention due to their unique properties and potential applications in various fields. Researchers have been dedicated to developing new synthesis methods and strategies to control the morphologies, compositions, and structures of metal nanoclusters. Through optimized synthesis methods, it is possible to prepare clusters with precise sizes and shapes, providing a solid foundation for subsequent research. Accurate determination of their crystal structures is crucial for understanding their behavior and designing custom functional materials. Dynamic metal nanoclusters also demonstrate potential applications in catalysis and optoelectronics. By manipulating the sizes, compositions, and surface structures of the clusters, efficient catalysts and optoelectronic materials can be designed and synthesized for various chemical reactions and energy conversion processes. This review summarizes the research progress in the synthesis methods, crystal structure characterization, and potential applications of dynamic metal nanoclusters. Various nanoclusters composed of different metal elements are introduced, and their potential applications in catalysis, optics, electronics, and energy storage are discussed. Additionally, the important role of dynamic metal nanoclusters in materials science and nanotechnology is explored, along with an overview of the future directions and challenges in this field. [ABSTRACT FROM AUTHOR]

Crystal structure predictions based on first-principles calculations have gained great success in materials science and solid state physics. However, the remaining challenges still limit their applications in systems with a large number of atoms, especially the complexity of conformational space and the cost of local optimizations for big systems. Here, we introduce a crystal structure prediction method, MAGUS, based on the evolutionary algorithm, which addresses the above challenges with machine learning and graph theory. Techniques used in the program are summarized in detail and benchmark tests are provided. With intensive tests, we demonstrate that on-the-fly machine-learning potentials can be used to significantly reduce the number of expensive first-principles calculations, and the crystal decomposition based on graph theory can efficiently decrease the required configurations in order to find the target structures. We also summarized the representative applications of this method on several research topics, including unexpected compounds in the interior of planets and their exotic states at high pressure and high temperature (superionic, plastic, partially diffusive state, etc.); new functional materials (superhard, high-energy-density, superconducting, photoelectric materials), etc. These successful applications demonstrated that MAGUS code can help to accelerate the discovery of interesting materials and phenomena, as well as the significant value of crystal structure predictions in general. [ABSTRACT FROM AUTHOR]

High-throughput data generation methods and machine learning (ML) algorithms have given rise to a new era of computational materials science by learning the relations between composition, structure, and properties and by exploiting such relations for design. However, to build these connections, materials data must be translated into a numerical form, called a representation, that can be processed by an ML model. Data sets in materials science vary in format (ranging from images to spectra), size, and fidelity. Predictive models vary in scope and properties of interest. Here, we review context-dependent strategies for constructing representations that enable the use of materials as inputs or outputs for ML models. Furthermore, we discuss how modern ML techniques can learn representations from data and transfer chemical and physical information between tasks. Finally, we outline high-impact questions that have not been fully resolved and thus require further investigation. [ABSTRACT FROM AUTHOR]

A cyclobenzaprine-tetraphenylborate (CBP-TPB) complex was synthesized, achieving a 78% yield through an anion exchange reaction. The white crystals of the complex were formed in acetonitrile and characterized using a variety of spectroscopic and analytical techniques, including ultraviolet, infrared, mass, elemental, and nuclear magnetic resonance (NMR) spectroscopy, as well as X-ray crystallography. The study employed a comprehensive approach to investigate the structural properties, stability, and behavior of the CBP-TPB complex. The use of crystallographic analysis, Hirshfeld surface analysis, quantum theory of atoms in molecules, noncovalent interaction reduced density gradient, global reactivity descriptors, frontier molecular orbitals, molecular electrostatic potential, and ultraviolet-visible spectroscopy provided valuable insights into the complex's molecular geometries, supramolecular features, and intermolecular interactions. These findings contribute to a better understanding of the CBP-TPB complex's potential applications in fields such as pharmaceuticals and materials science and emphasize the importance of combining theoretical predictions and experimental measurements in understanding molecular properties. The study also demonstrated the potential of density functional theory-based computational methods for predicting NMR spectroscopic parameters. [ABSTRACT FROM AUTHOR]

Since chemistry, materials science, and crystallography deal with three-dimensional structures, they use mathematics such as geometry and symmetry. In recent years, the application of topology and mathematics to material design has yielded remarkable results. It can also be seen that differential geometry has been applied to various fields of chemistry for a relatively long time. There is also the possibility of using new mathematics, such as the crystal structure database, which represents big data, for computational chemistry (Hirshfeld surface analysis). On the other hand, group theory (space group and point group) is useful for crystal structures, including determining their electronic properties and the symmetries of molecules with relatively high symmetry. However, these strengths are not exhibited in the low-symmetry molecules that are actually handled. A new use of mathematics for chemical research is required that is suitable for the age when computational chemistry and artificial intelligence can be used. [ABSTRACT FROM AUTHOR]

Developing new all‐visible‐light driven fluorescent molecular switches has attracted much interests due to their important application prospects in materials science and life science. Herein, three novel all‐visible‐light triggered fluorescent molecular photoswitches (2P3, 4F, and 4Cl) are designed and prepared, which are realized by prolonging π‐conjugation length with aggregated‐induced emission units. These photoswitches show reversible photochromic and fluorescent switching in dilute solutions and films under 420 nm and 560 nm irradiations. Meanwhile, upon alternating irradiations with 450 nm/560 nm light, effective photochromic and fluorescent switching in powder states are also achieved. More interestingly, two distinct conformations with different photochromic abilities can be altered by solvents polarity. The size difference of the two conformations is over 5 Å in the vertical direction which is one of the largest changes among photochromic molecules based on dithienylethene. The accompanying conformational changes are analysed to understand the relationship between solvent characteristics and crystal structure, as well as the intermolecular interaction in solid state for all‐visible‐light triggered photoswitching molecules. [ABSTRACT FROM AUTHOR]

Less conventional non-covalent interactions such as chalcogen bonds attract the attention of researchers in various fields (organocatalysis, material sciences, biological chemistry, ...). We present here useful descriptors to easily discriminate the structures in which chalcogen bonds involving selenium are observed. Our study focused on organoselenium compounds as chalcogen bond donors and on molecular entities, as chalcogen bond acceptors, containing N, O, S, Se, and Te atoms or aromatic rings. For conventional chalcogen bonds (C–Se⋯X, with X = N, O, S, Se, or Te), the combination of the C–Se⋯X angle and the distance between X and the C–Se-C plane proved to be most relevant for identification of chalcogen bonds. For chalcogen⋯π bonds, the most relevant parameters are a combination of the C–Se⋯X angle and the angle between the C–Se bond and the normal to the aromatic ring plane. [ABSTRACT FROM AUTHOR]

In materials science, crystal lattice structures are the primary metrics used to measure the structure–property paradigm of a crystal structure. Crystal compounds are understood by the number of various atomic chemical settings, which are associated with Wyckoff sites. In crystallography, a Wyckoff site is a point of conjugate symmetry. Therefore, features associated with the various atomic settings in a crystal can be fed into the input layers of deep learning models. Methods to analyze crystals using Wyckoff sites can help to predict crystal structures. Hence, the main contribution of our article is the classification of crystal classes using Wyckoff sites. The presented model classifies crystals using diffraction images and a deep learning method. The model extracts feature groups including crystal Wyckoff features and crystal geometry. In this article, we present a deep learning model to predict the stage of the crystal structure–property. The lattice parameters and the structure–property commotion values are used as inputs into the deep learning model for training. The structure–property value of a crystal with a lattice width value of one-half millimeter on average is used for learning. The model attains a considerable increase in speed and precision for the real structure–property prediction. The experimental results prove that our proposed model has a fast learning curve, and can have a key role in predicting the structure–property of compound structures. [ABSTRACT FROM AUTHOR]

The investigation of molecular crystals at high pressure is a sought-after trend in crystallography, pharmaceutics, solid state chemistry, and materials sciences. The di-p-tolyl disulfide (CH3−C6H4−S−)2 system is a bright example of high-pressure polymorphism. It contains "conventional" solid–solid transition and a "hidden" form which may be obtained only from solution at elevated pressure. In this work, we apply force field and periodic DFT computational techniques to evaluate the thermodynamic stability of three di-p-tolyl disulfide polymorphs as a function of pressure. Theoretical pressures and driving forces for polymorphic transitions are defined, showing that the compressibility of the γ phase is the key point for higher stability at elevated pressures. Transition state energies are also estimated for α → β and α → γ transitions from thermodynamic characteristics of crystal structures, not exceeding 5 kJ/mol. The β → γ transition does not occur experimentally in the 0.0–2.8 GPa pressure range because transition state energy is greater than 18 kJ/mol. Relations between free Gibbs energy (in assumption of enthalpy) of phases α, β, and γ, as a function of pressure, are suggested to supplement and refine experimental data. A brief discussion of the computational techniques used for high-pressure phase transitions is provided. [ABSTRACT FROM AUTHOR]

Keywords: coordination polymer; crystal structure; zinc complex EN coordination polymer crystal structure zinc complex 561 564 4 07/18/22 20220701 NES 220701 1 Introduction The continuous search and synthesis of novel coordination polymers (CPs) with unique structures and properties are dominantly driven by their fascinating structural and compositional diversities and manifold potential applications in catalysis [[1]], [[2]], [[3]], magnetism [[4]], [[5]], [[6]], [[7]], [[8]], [[9]], sensors [[10]], [[11]], [[12]], [[13]], adsorption/separation [[14]], and material science [[15]], [[16]], [[17]], [[18]], [[19]], [[20]]. 20 Yang, X.-G., Zhai, Z.-M., Lu, X.-M., Zhao, Y., Chang, X.-H., Ma, L.-F. Dalton Trans. 2019, 48, 10785-10789; https://doi.org/10.1039/c9dt02178g. 22 Chang, X.-H., Ling, X.-L., Lu, X.-M., Yang, X.-G., Li, F.-F., Guo, Y.-M. [Extracted from the article]

Structure–property relationships form the basis of many design rules in materials science, including synthesizability and long-term stability of catalysts, control of electrical and optoelectronic behavior in semiconductors, as well as the capacity of and transport properties in cathode materials for rechargeable batteries. The immediate atomic environments (i.e., the first coordination shells) of a few atomic sites are often a key factor in achieving a desired property. Some of the most frequently encountered coordination patterns are tetrahedra, octahedra, body and face-centered cubic as well as hexagonal close packed-like environments. Here, we showcase the usefulness of local order parameters to identify these basic structural motifs in inorganic solid materials by developing classification criteria. We introduce a systematic testing framework, the Einstein crystal test rig, that probes the response of order parameters to distortions in perfect motifs to validate our approach. Subsequently, we highlight three important application cases. First, we map basic crystal structure information of a large materials database in an intuitive manner by screening the Materials Project (MP) database (61,422 compounds) for element-specific motif distributions. Second, we use the structure-motif recognition capabilities to automatically find interstitials in metals, semiconductor, and insulator materials. Our Interstitialcy Finding Tool (InFiT) facilitates high-throughput screenings of defect properties. Third, the order parameters are reliable and compact quantitative structure descriptors for characterizing diffusion hops of intercalants as our example of magnesium in MnO2-spinel indicates. Finally, the tools developed in our work are readily and freely available as software implementations in the pymatgen library, and we expect them to be further applied to machine-learning approaches for emerging applications in materials science.

Structure–property relationships form the basis of many design rules in materials science, including synthesizability and long-term stability of catalysts, control of electrical and optoelectronic behavior in semiconductors, as well as the capacity of and transport properties in cathode materials for rechargeable batteries. The immediate atomic environments (i.e., the first coordination shells) of a few atomic sites are often a key factor in achieving a desired property. Some of the most frequently encountered coordination patterns are tetrahedra, octahedra, body and face-centered cubic as well as hexagonal close packed-like environments. Here, we showcase the usefulness of local order parameters to identify these basic structural motifs in inorganic solid materials by developing classification criteria. We introduce a systematic testing framework, the Einstein crystal test rig, that probes the response of order parameters to distortions in perfect motifs to validate our approach. Subsequently, we highlight three important application cases. First, we map basic crystal structure information of a large materials database in an intuitive manner by screening the Materials Project (MP) database (61,422 compounds) for element-specific motif distributions. Second, we use the structure-motif recognition capabilities to automatically find interstitials in metals, semiconductor, and insulator materials. Our Interstitialcy Finding Tool (InFiT) facilitates high-throughput screenings of defect properties. Third, the order parameters are reliable and compact quantitative structure descriptors for characterizing diffusion hops of intercalants as our example of magnesium in MnO2-spinel indicates. Finally, the tools developed in our work are readily and freely available as software implementations in the pymatgen library, and we expect them to be further applied to machine-learning approaches for emerging applications in materials science.

Analysis 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. [ABSTRACT FROM AUTHOR]

Understanding crystal structures and behaviors is crucial for constructing and engineering crystalline materials with various properties and functions. Recent advancement in three-dimensional electron diffraction (3D ED) and its application on structural characterizations have expanded single-crystal analysis into nano-sized materials. Herein, we provide an overview on 3D ED, including its development, data collection protocols, and their applications for investigating crystal structures. We focus on metal-organic frameworks (MOFs) and small-molecule-based organic crystals, and highlight the insights provided by 3D ED such as structure-property relationships, polymorphism, hydrogen bonding, and crystal chirality, which are crucial subjects in crystal engineering. With more and more laboratories setting up 3D ED techniques, we envision that it will not only continue providing critical structural information, but also establish a wide impact on chemistry, materials science and life science. [ABSTRACT FROM AUTHOR]

Exploration of π‐conjugated polycycles, particularly those have π‐frameworks spread over the three‐dimensional space, is essential in materials science and synthetic chemistry as these chemical entities possess featured optoelectronic properties and supramolecular assembly. Herein, the bowl‐shaped trichalcogenasumanenes are fused onto three branches of triptycene through pyrazine units, affording waterwheel‐like three‐dimensional polycycles 4 a/4 b. Because the three branches on 4 a/4 b are chemically equal, the molecular orbitals of 4 a/4 b show degenerate feature that results in the strong UV‐Vis absorbance at steady state. 4 a/4 b exhibit photo‐induced charge‐separation and subsequent charge‐redistribution at transient state, leading to excited state absorption in NIR‐II window (1165–1400 nm). 4 a/4 b are excellent fullerene receptors, and they form 1 : 1 host‐guest complexes with C60/C70 as proved by spectroscopic titrations and single crystal structure analysis. Moreover, 4 a/4 b show much stronger affinity toward C70 than C60. Consequently, 4 a/4 b are able to separate C60 and C70 from their mixture, giving the purity of C60 up to 99.5 %. [ABSTRACT FROM AUTHOR]

Recently, high-entropy alloys (HEAs) and HEA-type compounds have been extensively studied in the fields of material science and engineering. In this article, we report on the synthesis of a layered system MBi2Te4 where the M site possesses low-, middle-, and high-entropy states. The samples with M = Pb, Ag1/3Pb1/3Bi1/3, and Ag1/5In1/5Sn1/5Pb1/5Bi1/5 were newly synthesized and the crystal structure was examined by synchrotron X-ray diffraction and Rietveld refinement. We found that the M-Te2 distance was systematically compressed with decreasing lattice constants, where the configurational entropy of mixing at the M site is also systematically increased. The details of structural refinements and the electrical transport property are presented. [ABSTRACT FROM AUTHOR]

The Materials Science (MS) beamline at SESAME (Synchrotron‐light for Experimental Science and Applications in the Middle East), dedicated to the X‐ray powder diffraction technique, started its operational phase in December 2020 by hosting its first users. The MS endstation comprises a two‐circle diffractometer coupled with a PILATUS 300K area detector, with which direct beam images are collected and compared with the initial ray‐tracing simulation results. We present a detailed description of the beamline components and the experimental characterization of the main instrumental parameters relying on the instrumental profile and the angular resolution. A representative example for microstructure investigations of a nanocrystalline material is demonstrated. [ABSTRACT FROM AUTHOR]

We examine the impact of writing-to-learn (WTL) on promoting conceptual understanding of introductory materials science and engineering, including crystal structures, stress–strain behavior, phase diagrams, and corrosion. We use an analysis of writing products in comparison with pre/post concept-inventory-style assessments. For all topics, statistically significant improvements between draft and revision scores are apparent. For the stress–strain and phase diagram WTL assignments that require synthesis of qualitative data into quantitative formats, while emphasizing microstructure-properties correlations, the highest WTL effect sizes and medium-to-high gains on corresponding assessments are observed. We present these findings and suggest strategies for future WTL design and implementation. [ABSTRACT FROM AUTHOR]

Metal salphen containing complexes have found a broad variety of applications in catalysis as well as material science. Most of these materials are either mono‐nuclear complexes or polymeric network materials. Discrete macromolecular polynuclear salphens are still rare and mostly two‐dimensional structures. Here we present three‐dimensional trinuclear Ni(II)‐ and Pt(II)‐cage compounds with a covalently bound [2+3] salicylimine cage backbone. The solubility of the cages allowed their investigation by spectroscopic methods. Furthermore, the Pt(II) derivative was investigated by single crystal X‐ray structure analysis revealing that the presented cage compounds possess the largest intrinsic void of metal salphen cages reported till date. [ABSTRACT FROM AUTHOR]

In the title compound, two tert-butyl groups replace the two hydrogen atoms at the C2 and C3 positions of the cyclohexadiene, respectively, and the electrophilic ester group replaces the hydrogen atom at the benzene ring position, as shown in the figure. Since all hydrogen atoms in this structure are not conformational isomers, * is used to distinguish all hydrogen atoms. [Extracted from the article]

The Mn3X (where X = Ga, Ge, Sn, etc.) compounds have appealing prospects for spintronic applications due to their various crystal structures and magnetic properties for the design of reliable high-density memories. However, controlled growth of high-quality Mn3X thin films remains challenging in material science. Here, we reported the controlled film growth of Heusler alloy Mn3Ge, which could crystallize in respective tetragonal and hexagonal structures. The tetragonal D022-type Mn3Ge film exhibits strong perpendicular ferromagnetic anisotropy, while the hexagonal D019-type Mn3Ge film indicates non-collinear triangular antiferromagnetic order. From our experimental observations of structure characterizations, magnetic properties, anomalous Hall effect, and magnetoresistance measurements, we realized the manipulation of spin orientations and topological features. Majority/minority spin polarized Fermi surface and density of states of both tetragonal and hexagonal Mn3Ge structures were investigated by density functional theory calculations. Our work not only opens up technology routes toward the development of Mn3X-based devices for applications in topological spintronics and spin-torque memories but also leads to engineer the physical properties for fundamental study. [ABSTRACT FROM AUTHOR]

Due to their ability to recognize complex patterns, neural networks can drive a paradigm shift in the analysis of materials science data. Here, we introduce ARISE, a crystal-structure identification method based on Bayesian deep learning. As a major step forward, ARISE is robust to structural noise and can treat more than 100 crystal structures, a number that can be extended on demand. While being trained on ideal structures only, ARISE correctly characterizes strongly perturbed single- and polycrystalline systems, from both synthetic and experimental resources. The probabilistic nature of the Bayesian-deep-learning model allows to obtain principled uncertainty estimates, which are found to be correlated with crystalline order of metallic nanoparticles in electron tomography experiments. Applying unsupervised learning to the internal neural-network representations reveals grain boundaries and (unapparent) structural regions sharing easily interpretable geometrical properties. This work enables the hitherto hindered analysis of noisy atomic structural data from computations or experiments. The present manuscript reports a Bayesian deep-learning approach for the automatic, robust classification of polycrystalline systems of both synthetic and experimental origin. The unsupervised analysis of the internal neural-network representations reveals physically understandable patterns. [ABSTRACT FROM AUTHOR]

Crystal Structure Prediction (CSP) is one of the central and most challenging problems in materials science and computational chemistry. In CSP, the goal is to find a configuration of ions in 3D space that yields the lowest potential energy. Finding an efficient procedure to solve this complex optimisation question is a well known open problem. Due to the exponentially large search space, the problem has been referred in several materials-science papers as "NP-Hard and very challenging" without a formal proof. This paper fills a gap in the literature providing the first set of formally proven NP-Hardness results for a variant of CSP with various realistic constraints. In particular, we focus on the problem of removal: the goal is to find a substructure with minimal potential energy, by removing a subset of the ions. Our main contributions are NP-Hardness results for the CSP removal problem, new embeddings of combinatorial graph problems into geometrical settings, and a more systematic exploration of the energy function to reveal the complexity of CSP. In a wider context, our results contribute to the analysis of computational problems for weighted graphs embedded into the three-dimensional Euclidean space. [ABSTRACT FROM AUTHOR]

As an important tool to study the microstructure and properties of materials, High Resolution Transmission Electron Microscope (HRTEM) images can obtain the lattice fringe image (reflecting the crystal plane spacing information), structure image and individual atom image (which reflects the configuration of atoms or atomic groups in crystal structure). Despite the rapid development of HTTEM devices, HRTEM images still have limited achievable resolution for human visual system. With the rapid development of deep learning technology in recent years, researchers are actively exploring the Super-resolution (SR) model based on deep learning, and the model has reached the current best level in various SR benchmarks. Using SR to reconstruct high-resolution HRTEM image is helpful to the material science research. However, there is one core issue that has not been resolved: most of these super-resolution methods require the training data to exist in pairs. In actual scenarios, especially for HRTEM images, there are no corresponding HR images. To reconstruct high quality HRTEM image, a novel Super-Resolution architecture for HRTEM images is proposed in this paper. Borrowing the idea from Dual Regression Networks (DRN), we introduce an additional dual regression structure to ESRGAN, by training the model with unpaired HRTEM images and paired nature images. Results of extensive benchmark experiments demonstrate that the proposed method achieves better performance than the most resent SISR methods with both quantitative and visual results. [ABSTRACT FROM AUTHOR]

Three single crystals of CaTi[sub 2]O4 (CT)-type, CaCaFe2O4 (CF)-type, and new low-density CaFe2O4 (LD-CF) related MgAl2O4 were synthesized at 27 GPa and 2500 °C and also CT-type MgAl2O4 at 45 GPa and 1727 °C using conventional and advanced multi-anvil technologies, respectively. The structures of CT-type and LD-CF related MgAl2O4 were analyzed by single-crystal X-ray diffraction. The lattice parameters of the CT-type phases synthesized at 27 and 45 GPa were a = 2.7903(4), b = 9.2132(10), and c = 9.3968(12) Å, and a = 2.7982(6), b = 9.2532(15), and c = 9.4461(16) Å, respectively, (Z = 4, space group: Cmcm) at ambient conditions. This phase has an AlO6 octahedral site and an MgO8 bicapped trigonal prism with two longer cation-oxygen bonds. The LD-CF related phase has a novel structure with orthorhombic symmetry (space group: Pnma), and lattice parameters of a = 9.207(2), b = 3.0118(6), and c = 9.739(2) Å (Z = 4). The structural framework comprises tunnel-shaped spaces constructed by edge- and corner-sharing of AlO6 and a 4+1 AlO5 trigonal bipyramid, in which MgO5 trigonal bipyramids are accommodated. The CF-type MgAl2O4 also has the same space group of Pnma but a slightly different atomic arrangement, with Mg and Al coordination numbers of 8 and 6, respectively. The LD-CF related phase has the lowest density of 3.50 g/cm³ among MgAl2O4 polymorphs, despite its high-pressure synthesis from the spinel-type phase (3.58 g/cm³), indicating that the LD-CF related phase formed via back-transformation from a high-pressure phase during the recovery. Combined with the previously determined phase relations, the phase transition between CF-and CT-type MgAl2O4 is expected to have a steep Clapeyron slope. Therefore, CT-type phase may be stable in basaltic- and continental-crust compositions at higher temperatures than the average mantle geotherm in the wide pressure range of the lower mantle. The LD-CF related phase could be found in shocked meteorites and used for estimating shock conditions. [ABSTRACT FROM AUTHOR]