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Exceptional molecules and materials with one (or more) extraordinary properties are both technologically valuable and fundamentally interesting because they often involve new physical phenomena or new compositions that defy expectations. Historically, exceptionality has been achieved through serendipity, but recently, machine learning (ML) and automated experimentation have been widely proposed to accelerate target identification and synthesis planning. In this Perspective, we argue that the data-driven methods commonly used today are well-suited for optimization but not for realizing new exceptional materials or molecules. Finding such outliers should be possible using ML, but only by shifting away from using traditional ML approaches that tweak the composition, crystal structure, or reaction pathway. We highlight case studies of high-Tc superconductors and superhard materials to demonstrate the challenges of ML-guided discovery and discuss the limitations of automation for this task. We then provide six recommendations for the development of ML methods capable of exceptional materials discovery: (i) Avoid the tyranny of the middle and focus on extrema; (ii) When data is limited, qualitative predictions that provide direction are more valuable than interpolative accuracy; (iii) Sample what can be made and how to make it, and defer optimization; (iv) Create room (and look) for the unexpected while pursuing your goal; (v) Try to fill-in-the-blanks of input and output space; (vi) Do not confuse human understanding with model interpretability. We conclude with a description of how these recommendations can be integrated into automated discovery workflows that should enable the discovery of exceptional molecules and materials.

The lateral flow assay (LFA) is a point-of-care diagnostic test commonly available in an over-the-counter format because of its simplicity, speed, low cost, and portability. The reporter particles in these assays are among their most significant components because they perform the diagnostic readout and dictate the test’s sensitivity. Today, gold nanoparticles are frequently used as reporters, but recent work focusing on photoluminescent-based reporter technologies has pushed LFAs to better performance. These efforts have focused specifically on reporters made of organic fluorophores, quantum dots, lanthanide chelates, persistent luminescent phosphors, and upconversion phosphors. In most cases, photoluminescent reporters show enhanced sensitivity compared to conventional gold nanoparticle-based assays. Here, we examine the advantages and disadvantages of these different reporters and highlight their potential benefits in LFAs. Our assessment shows that photoluminescent-based LFAs can not only reach lower detection limits than LFAs with traditional reporters, but they also can be capable of quantitative and multiplex analyte detection. As a result, the photoluminescent reporters make LFAs well-suited for medical diagnostics, the food and agricultural industry, and environmental testing.

Monitors, televisions, and smartphones have been revolutionized by the invention of InGaN-based LED backlighting. Yet, regular exposure to the bright blue light emitted by these screens can lead to mood disorders and insomnia. Switching to a "human-centric" display that uses a violet-emitting LED chip and a trichromatic phosphor blend is one materials chemistry solution for this problem. The challenge is finding a blue-emitting phosphor with a sufficiently small Stokes’ shift to down-convert the violet LED light. This study reports a new oxynitride phosphor that meets this need. K3AlP3O9N:Eu2+ exhibits a narrow (45 nm, 2206 cm-1), thermally robust, and efficient blue photoluminescence upon violet excitation. Computational modeling and photoluminescence spectroscopy revealed that a rare combination of preferential excitation and site-selective quenching produce the desired optical properties. Combining K3AlP3O9N:Eu2+ with red- and green-emitting phosphors and a violet LED covers 10% more of the color gamut than a commercial tablet while simultaneously mitigating blue light exposure en route to next-generation, human-centric displays.

Luminescent materials are continually sought for application in solid-state LED-based lighting and display applications. This has traditionally required extensive experimental effort. More recently, the employment of data-driven approaches in materials science has provided an alternative avenue to accelerate the discovery and development of luminescent materials. In this Perspective, we give an overview of luminescent materials used for lighting and display applications with a specific focus on inorganic phosphors, quantum dots, and organic light-emitting diodes. We discuss recent progress using data-driven approaches to discover new compounds, predict optical properties, and optimize synthesis, among other topics for each type of material. We then highlight future research directions focusing on using artificial intelligence (AI) to advance these fields and address some cross-cutting challenges limiting the current application of AI techniques in luminescence-related research.

The development of structural materials with outstanding mechanical response has long been sought for innumerable industrial, technological, and even biomedical applications. However, these compounds tend to derive their fascinating properties from a myriad of interactions spanning multiple scales, from localized chemical bonding to macroscopic interactions between grains. This diversity has limited the ability of researchers to develop new materials on a reasonable timeline. Fortunately, the advent of machine learning in materials science has provided a new approach to analyze high-dimensional space and identify correlations among the structure-composition-property-processing relationships that may have been previously missed. In this review, we examine some successful examples of using data science to improve known structural materials by analyzing fatigue and failure, and we discuss approaches to develop entirely new classes of structural materials in complex composition spaces including high-entropy alloys and bulk metallic glasses. Highlighting the recent advancement in this field demonstrates the power of data-driven methodologies that will hopefully lead to the production of market-ready structural materials.

Summary This work presents an approach to aid the discovery of inorganic solids by highlighting regions of underexplored yet likely productive composition space using machine learning. A support vector regression algorithm was constructed to determine a compound's formation energy based solely on chemical composition using 313,965 high-throughput density functional theory calculations. The resulting predicted formation energies were then used to construct zero-kelvin convex hull diagrams and identify compositions on the hull and +50 meV above the convex hull. Using this methodology, Y-Ag-Tr (Tr = B, Al, Ga, In) ternary diagrams were explored owing to the diversity of chemistries as a function of triel element to provide experimental validation of the predictions. A particularly promising but unexplored region in the Y-Ag-In diagram was identified, and the ensuing solid-state synthesis produced YAg0.65In1.35, which has not been reported. First-principle calculations were finally used to determine the ordering of Ag and In and confirm the crystal structure solution.

This Methods/Protocols article is intended for materials scientists interested in performing machine learning-centered research. We cover broad guidelines and best practices regarding the obtaining...

In this paper, the photoluminescent properties of a lead-free double perovskite Cs2NaInCl6 doped with Sb3+ are explored. The host crystal structure is a cubic double perovskite with Fmm symmetry, a = 10.53344(4) A, and rock salt ordering of Na+ and In3+. It is a wide bandgap compound (Eg ≈ 5.1 eV), and substitution with Sb3+ leads to strong absorption in the UV due to localized 5s2 → 5s15p1 transitions on Sb3+ centers. Radiative relaxation back to the 5s2 ground state, via a 3P1 → 1S0 transition, leads to intense blue luminescence, centered at 445 nm, with a photoluminescent quantum yield of 79%. The Stokes shift of 0.94 eV is roughly 33% smaller than it is in the related vacancy ordered double perovskite Cs2SnCl6. The reduction in Stokes shift is likely due to a change in coordination number of Sb3+ from 6-coordinate in Cs2NaInCl6 to 5-coordinate in Cs2SnCl6. In addition to the high quantum yield, Cs2NaInCl6:Sb3+ exhibits excellent air/moisture stability and can be prepared from solution; these characteristics make it a promising blue phosphor for applications involving near-UV excitation.

Incorporating two persistent luminescent nanophosphors (PLNPs), green-emitting SrAl(2)O(4):Eu(2+),Dy(3+) (SAO) and blue-emitting (Sr(0.625)Ba(0.375))(2)MgSi(2)O(7):Eu(2+),Dy(3+) (SBMSO), in a single lateral flow assay (LFA) establishes a luminescence-based, multiplex point-of-need test capable of simultaneously detecting two different analytes in a single sample. The advantages of this system are the high sensitivity and photostability of PLNPs, while only requiring access to minimal hardware and a smartphone for signal detection. The PLNPs were obtained by first wet milling bulk synthesized phosphor powders, followed by fractionation using differential centrifugal sedimentation to obtain monodisperse nanoparticles. A modified Stöber process was then employed to encapsulate the nanoparticles in a water-stable silica shell followed by attaching antibodies to the particles’ surfaces using reductive amination chemistry. The resulting PLNPs were incorporated in an LFA to concurrently detect two independent model analytes, prostate-specific antigen (PSA) and human chorionic gonadotropin (hCG). The multicolor-multiplex PLNP-based assays were finally imaged using a smartphone-based imaging system with excellent detection limits (0.1 ng mL(−1) of PSA and 1 ng mL(−1) of hCG) that are competitive with commercially available LFAs.

Polar intermetallics are an intriguing class of compounds with complex relationships between composition and structure that are not fully understood. This work reports a systematic study of the underexplored ternary composition space RE–Au–Tt (RE = La, Ce, Pr, Nd, Ho; Tt = Ge, Sn, Pb) to expand our knowledge of the intriguing chemistry and diversity achievable with these metallic constituents. These composition spaces are particularly interesting because of the potential to find Au-bearing, highly polar intermetallic compounds. The elements were first reacted through arc welding under an inert atmosphere followed by annealing at 850°C. X-ray diffraction of the products identified unreported eight compounds ranging from the simple NaTl-type compounds La1.5Au2Pb0.5, Nd2-xAu2Pbx, and Ho2-xAu2Snx, to the more structurally complex La5AuPb3 in the Hf5CuSn3-type structure and Pu3Pd4-type RE3Au3Ge (RE = La, Ce, Pr, Nd). First-principles electronic structure calculations revealed that a combination of Fermi surface-Brillouin zone interactions, electrostatic interactions, and delocalized metallic bonding contributes to the formation of these phases. These calculations show that a mixture of electrostatic and metallic bonding plays a dominant role in these phases. The RE–Au–Tt composition space remains full of potential for discovering materials with relevant magnetic and quantum properties, provided the crystal chemistry can be comprehended.

The Mo2-xWxBC system is of interest as a material with high hardness while maintaining moderate ductility. In this work, synchrotron diffraction experiments are performed on Mo2-xWxBC solid solutions, where x = 0, 0.5, and 0.75, upon hydrostatic compression to ~54 GPa, ~55 GPa, and ~60 GPa, respectively. Trends in bulk modulus, K0, are evaluated by fitting collected pressure-volume data with a third-order Birch-Murnaghan equation of state, finding K0 = 333(9) GPa for Mo2BC, K0 = 335(11) GPa for Mo1:5W0:5BC, and K0 = 343(8) GPa for Mo1:25W0:75BC. While K0 demonstrates a slight increase when Mo is substituted by W, calculated zero pressure unit cell volume, V0, exhibits the opposite trend. The decrease in V0 corresponds to an increase in valence electron density, hardness, and K0. Observations corroborate previously reported computational results and will inform future efforts to design sustainable materials with exceptional mechanical properties.

The existence of two compounds with the formula A3Ir (A = Rb, Cs) were predicted to form at high pressure by constructing convex hulls for the Rb−Ir and Cs−Ir binary systems. These phases were identified using a nonbiased automatic structure search method based on Density Functional Theory accompanied by a particle swarm optimization (PSO) algorithm. Increasing the applied pressure above ≈10 GPa indicated a favorable formation enthalpy ( Δ H ) for Rb3Ir with the Cu3Ti structure type (space group Pmnm; No. 59) and Cs3Ir in the Ni3Ta structure type (space group P21/m; No. 11). Phonon calculations showed both compositions are dynamically stable in these structure types. Electronic structure calculations revealed that Rb3Ir is a semiconductor with a ≈1.3 eV band gap, calculated using a hybrid exchange-correlation functional, whereas the Fermi level for Cs3Ir falls into a deep pseudogap, suggesting poor metallic character. The chemical bonding in both phases is dominated by the A–Ir (A = Rb, Cs) interactions with the contacts more significant in Cs3Ir than in the Rb3Ir crystal structure. These high-pressure phases lastly showed unusual oxidation states on the transition metal. The charge on Ir is calculated to surpass −1 due to charge transfer between the alkali metal and iridium, implying the presence of an iridide anion at high pressure.

This Editorial is intended for materials scientists interested in performing machine learning-centered research.We cover broad guidelines and best practices regarding the obtaining and treatment of data, feature engineering, model training, validation, evaluation and comparison, popular repositories for materials data and benchmarking datasets, model and architecture sharing, and finally publication.In addition, we include interactive Jupyter notebooks with example Python code to demonstrate some of the concepts, workflows, and best practices discussed.Overall, the data-driven methods and machine learning workflows and considerations are presented in a simple way, allowing interested readers to more intelligently guide their machine learning research using the suggested references, best practices, and their own materials domain expertise.

This work presents an approach to aid the discovery of novel inorganic solids by highlighting regions of underexplored, yet likely productive composition space using machine learning. A support vector regression (SVR) algorithm was constructed first to determine a compound’s formation energy (∆𝐸𝑓,SVR) based solely on chemical composition using data from 313,965 high-throughput density functional theory calculations. The resulting predicted formation energies (r2 = 0.94; MAE = 8.5 meV/atom) were then used to construct zero-kelvin convex hull diagrams and identify compositions immediately on the hull, as well as +50 meV above the convex hull to capture potential compounds that are considered energetically unfavorable but that are still experimentally accessible. Using this methodology, four ternary composition diagrams, Y−Ag−Tr (Tr = B, Al, Ga, In), were explored owing to the diversity of chemistries as a function of triel element to provide experimental validation for the predictions. A particularly promising but unexplored region in the Y−Ag−In diagram was identified, and the ensuing solid-state high-temperature synthesis produced YAg0.65In1.35, which has not been reported. First-principle calculations were finally used to determine the ordering of Ag and In as well as confirm the crystal structure solution. Our combination of machine learning, inorganic synthesis, and computational modeling describes a new avenue where data-centric models and computation play a critical role in supporting the experimental examination of unexplored phase diagrams.

Rare-earth substituted inorganic phosphors are critical for solid state lighting. New phosphors are traditionally identified through chemical intuition or trial and error synthesis, inhibiting the discovery of potential high-performance materials. Here, we merge a support vector machine regression model to predict a phosphor host crystal structure’s Debye temperature, which is a proxy for photoluminescent quantum yield, with high-throughput density functional theory calculations to evaluate the band gap. This platform allows the identification of phosphors that may have otherwise been overlooked. Among the compounds with the highest Debye temperature and largest band gap, NaBaB9O15 shows outstanding potential. Following its synthesis and structural characterization, the structural rigidity is confirmed to stem from a unique corner sharing [B3O7]5– polyanionic backbone. Substituting this material with Eu2+ yields UV excitation bands and a narrow violet emission at 416 nm with a full-width at half-maximum of 34.5 nm. More importantly, NaBaB9O15:Eu2+ possesses a quantum yield of 95% and excellent thermal stability., Identifying phosphors with good thermal stability and quantum efficiency is a prerequisite to improve the performance of white LED light sources. Here, a combined machine learning and density functional theory method is introduced to identify next generation inorganic phosphors.

Highly efficient, thermally stable, narrow-band phosphors that can be excited by a blue LED chip are crucial for energy-efficient light bulbs and display lighting. Here, a rare narrow-band, green-emitting phosphor based on the compound NaBaB9O15:Eu2+ is demonstrated. The emission peak is centered at 515 nm with a full-width at half-maximum (fwhm) of 61 nm (2294 cm−1), and a photoluminescence quantum yield of >80% using blue or near-UV LED excitation. This borate’s remarkable green emission stems from Eu2+ substituting on the smaller [NaO6] polyhedron instead of the larger and more favorable [BaO9] polyhedron. The phosphor also exhibits negligible thermal quenching up to 650 K owing to the wide band gap, high connectivity of the rigid NaBaB9O15 crystal structure, and the depopulation of trap-states. This combination of optical properties and its straightforward synthesis conditions makes NaBaB9O15:Eu2+ an ideal green phosphor for next-generation LED-based lighting or display systems.

There remain 21 systems (out of over 3500 possible combinations of the elements) in which the existence of the simple binary equiatomic phases AB has not been established experimentally. Among these, the presumed binary phase HfIn is predicted to adopt the tetragonal CuAu-type structure (space group P4/mmm) by a recently developed machine-learning model and by structure optimization through global energy minimization. To test this prediction, the Hf−In system was investigated experimentally by reacting the elements in a 1:1 stoichiometry at 1070 K. Under the conditions investigated, the bulk and surface of the sample correspond to different crystalline phases but have nearly the same equiatomic composition, as revealed by energy-dispersive X-ray analysis. The structure of the bulk sample, which was solved from powder X-ray diffraction data through simulated annealing, corresponds to the γ-brass (Cu5Zn8) type (space group I43m) with Hf and In atoms disordered over four sites. The structure of crystals sel...

Following an investigation proving La2Si4N6C crystallizes in a monoclinic space group, isostructural to Y2Si4N6C, the reportedly hexagonal (La0.5Y0.5)2Si4N6C was reinvestigated to examine the apparent crystal structure change across the solid solution. Initially, calculating the electronic structure and phonon density of states of (La0.5Y0.5)2Si4N6C in the P63mc space group revealed an imaginary phonon mode, which is indicative of a structural instability. Displacing the atoms along the pathway of the imaginary vibration led to a previously unreported space group for carbonitridosilicates, trigonal P31c. The assignment of the trigonal space group was subsequently confirmed by synthesizing (La0.5Y0.5)2Si4N6C using high-temperature, solid state synthesis and analyzing the crystal structure with high-resolution synchrotron X-ray powder diffraction. Preparing the solid solution, (LaδY1−δ)1.98Ce0.02Si4N6C (δ = 0–0.5), showed that the crystal structure changes from the monoclinic to the trigonal space group at ...

Materials either have a high hardness or excellent ductility, but rarely both at the same time. ${\mathrm{Mo}}_{2}\mathrm{BC}$ is one of the only crystalline materials that simultaneously has a high Vickers hardness and is also moderately ductile. The origin of this unique balance is revealed here using stress-strain calculations. The results show an anisotropic nonlinear elastic response including an intermediate tensile strain-stiffening behavior and a two-step sequential failure under shear strain that resembles the behavior of soft materials such as biological systems or polymer networks rather than hard, refractory metals. The mechanism of the unusual nonlinear elasticity is established by analyzing changes in the electronic structure and the chemical bonding environments under mechanical perturbation. The optimized structure under extreme strain shows the formation of a pseudogap in DOS and dimerization of the structure's boron-boron zigzag chain. These mechanisms delay the ultimate failure, establishing a pathway for developing the next generation of structural materials with high hardness and ductility.

Single crystal diffraction is one of the most common experimental techniques in chemistry for determining a crystal structure. However, the process of crystal structure solution and refinement is not always straightforward. Methods to simplify and rationalize the path to the most optimal crystal structure model have been incorporated into various data processing and crystal structure solution software, with the focus generally on aiding macromolecular or protein structure solution. In this work, we propose a new method that uses single crystal data to solve the crystal structures of inorganic, extended solids called “Single Crystal Automated Refinement (SCAR).” The approach was developed using data mining and machine-learning methods and considers several structural features common in inorganic solids, like atom assignment based on physically reasonable distances, atomic statistical mixing, and crystallographic site deficiency. The output is a tree of possible solutions for the data set with a corresponding fit score indicating the most reasonable crystal structure. Here, the foundation for SCAR is presented followed by the implementation of SCAR to solve two newly synthesized and previously unreported phases, ZrAu0.5Os0.5 and Nd4Mn2AuGe4. The structure solutions are found to be comparable with manually solving the data set, including the same refined mixed occupancies and atomic deficiency, supporting the validity of this automatic structure solution method. The proposed SCAR program is thusly verified to be a fast and reliable assistant in solving even complex single crystal diffraction data for extended inorganic solids.

The budding field of materials informatics has coincided with a shift towards artificial intelligence to discover new solid-state compounds. The steady expansion of repositories for crystallographic and computational data has set the stage for developing data-driven models capable of predicting a bevy of physical properties. Machine learning methods, in particular, have already shown the ability to identify materials with near ideal properties for energy-related applications by screening crystal structure databases. However, examples of the data-guided discovery of entirely new, never-before-reported compounds remain limited. The critical step for determining if an unknown compound is synthetically accessible is obtaining the formation energy and constructing the associated convex hull. Fortunately, this information has become widely available through density functional theory (DFT) data repositories to the point that they can be used to develop machine learning models. In this Review, we discuss the specific design choices for developing a machine learning model capable of predicting formation energy, including the thermodynamic quantities governing material stability. We investigate several models presented in the literature that cover various possible architectures and feature sets and find that they have succeeded in uncovering new DFT-stable compounds and directing materials synthesis. To expand access to machine learning models for synthetic solid-state chemists, we additionally present MatLearn. This web-based application is intended to guide the exploration of a composition diagram towards regions likely to contain thermodynamically accessible inorganic compounds. Finally, we discuss the future of machine-learned formation energy and highlight the opportunities for improved predictive power toward the synthetic realization of new energy-related materials.

A series of Eu 2+ -substituted yellow-green emitting phosphors based on the compound, Sr 6 M 2 Al 4 O 15 ( M = Y, Lu, Sc) were identified as potential efficient phosphors based on their high calculated Debye temperatures (Θ D > 450 K), which acts as a proxy for photoluminescent quantum yield (PLQY). The crystal structure contains corner-sharing [ M O 6 ] octahedra and [AlO 4 ] tetrahedra leading to a highly connected, densely packed crystal structure. However, contrary to prediction, these compounds all showed a low PLQY ( T 50 ) occurring well below room temperature. These results suggest that even though Debye temperature (Θ D ) is a valid proxy for PLQY, it does not describe thermal quenching.

The crystal structure of a novel barium yttrium borate, Ba2Y5B5O17, was determined using a combination of ab initio global optimization algorithms and density functional theory calculations along with Rietveld refinement of high-resolution synchrotron X-ray powder diffraction data. Synthesized using a high-temperature solid-state route, the structure consists of edge- and corner-sharing Y- and Ba-centered polyhedra along with BO3 trigonal planes. Ba and Y occupy four crystallographically independent sites with two fully occupied by Y and two having a statistical mixture of Y and Ba. Substituting Ce3+ into the structure for Y3+ yields blue photoluminescence (λem = 443 nm) upon excitation with UV (λem = 365 nm) light. The emission of this new compound is efficient with an external quantum yield of 70% and is stable as a function of temperature with a quenching temperature of ≈400 K.

In this article we provide an overview of data mining, informatics, and machine learning approaches for thermoelectrics. We describe how the initial development of a thermoelectric materials database has enabled the creation of a recommendation engine governed by machine learning and how this engine introduces a new paradigm in thermoelectric materials development. Performance probability is generated based on training models. A demonstration of the data mining approach is set forth in a ternary intermetallic system, where we report new materials.

Information on the 5d level centroid shift (ϵc) of rare-earth ions is critical for determining the chemical shift and the Coulomb repulsion parameter as well as predicting the luminescence and thermal response of rare-earth substituted inorganic phosphors. The magnitude of ϵc depends on the binding strength between the rare-earth ion and its coordinating ligands, which is difficult to quantify a priori and makes phosphor design particularly challenging. In this work, a tree-based ensemble learning algorithm employing extreme gradient boosting is trained to predict ϵc by analyzing the optical properties of 160 Ce3+ substituted inorganic phosphors. The experimentally measured ϵc of these compounds was featurized using the materials' relative permittivity (ϵr), average electronegativity, average polarizability, and local geometry. Because the number of reported ϵr values is limited, it was necessary to utilize a predicted relative permittivity (ϵr,SVR) obtained from a support vector regressor trained on data from ∼2800 density functional theory calculations. The remaining features were compiled from open-source databases and by analyzing the rare-earth coordination environment from each Crystallographic Information File. The resulting ensemble model could reliably estimate ϵc and provide insight into the optical properties of Ce3+-activated inorganic phosphors.

In the quest to predict photoluminescent efficiency in rare-earth substituted inorganic phosphors, research has shown that materials consisting of ordered, rigid crystal structures tend to possess the highest photoluminescent quantum yields. A compound's Debye temperature (ΘD), which can be calculated using ab initio calculations, is an ideal proxy for quantitatively comparing structural rigidity among different inorganic compounds, allowing potentially efficient phosphors to be selected from large crystal structure databases. However, the high computational cost of these calculations limits estimating ΘD for unsubstituted host crystal structures only. It is assumed that the low substitution concentration of the rare-earth luminescent center does not significantly influence a material's Debye temperature. This work evaluates the validity of this approximation by examining the effect of luminescent center substitution on a host structure's ΘD. Two well-known phosphors, (Y1–xCex)3Al5O12 (x = 0 − 0.05) and Ba1-δEuδMgAl10O17 (δ = 0 − 0.15), were synthesized with varying rare-earth concentrations, while ΘD was computationally estimated and then determined by ultrasonic pulse-echo speed-of-sound and low-temperature heat capacity measurements. The ensuing results provide key implications for using ΘD as a proxy for structural rigidity in substituted inorganic compounds.

The average and local structure of the oxides Ba2SiO4, BaAl2O4, SrAl2O4, and Y2SiO5 are examined to evaluate crystal rigidity in light of recent studies suggesting that highly connected and rigid structures yield the best phosphor hosts. Simultaneous momentum-space refinements of synchrotron X-ray and neutron scattering yield accurate average crystal structures, with reliable atomic displacement parameters. The Debye temperature ΘD, which has proven to be a useful proxy for structural rigidity, is extracted from the experimental atomic displacement parameters and compared with predictions from density functional theory calculations and experimental low-temperature heat capacity measurements. The role of static disorder on the measured displacement parameters, and the resulting Debye temperatures, are also analyzed using pair distribution function of total neutron scattering, as refined over varying distance ranges of the pair distribution function. The interplay between optimal bonding in the structure, structural rigidity, and correlated motion in these structures is examined, and the different contributions are delineated.

1Replacing inefficient incandescent light bulbs with energy-efficient light emitting diode (LED) technology is one of the best opportunities to dramatically reduce energy use due to their outstanding efficiency. 2 LED lights have additional advantages of longer lifetimes compared to incandescent bulbs, they are compact in physical size, and they are composed of environmentally benign materials, unlike compact fluorescent bulbs that contain toxic heavy metals. This has led to the use of LEDs in a range of applications from indicator lights to display backlighting; yet, to truly impact global energy use, their use is necessary in homes as general white luminaries. There are three principle strategies for creating a useful broad spectrum white light from a monochromatic LED. 3,4 One employs multiple LEDs, e.g., red, green, and blue, in a single device, as illustrated in Fig. 1a. Although viable, the low efficiency and shorter lifetime of green LEDs along with the need for complex circuitry prevents the current widespread use of these devices. Another solution uses a UV (or near-UV) LED in combination with three (red, green, and blue) inorganic phosphors (Fig. 1b). Here, the narrow LED emission is completely converted to longer wavelengths by three rareearth substituted inorganic phosphors that when blended in the correct ratios produces white light. The third, and industry standard approach, is to generate white light by using rare-earth substituted (usually Ce 3+

The authors thank J. Siewenie for assistance with collection of the neutron powder diffraction data and Dr. Z. Gan for assistance with the 43Ca NMR measurements.; International audience; Calcium scandate (CaSc2O4) substituted with small amounts (

Polycrystalline samples of the boride series Ti{sub 9−x}M{sub 2+x}Ru{sub 18}B{sub 8} (M=Cr, Co, Mn, Ni) including single crystals of Ti{sub 8}Co{sub 3}Ru{sub 18}B{sub 8} have been prepared by arc-melting the elements. The phases were characterized by powder X-ray diffraction (Rietveld refinement), single-crystal X-ray diffraction (for M=Co), and energy-dispersive X-ray (EDX) analysis. They are substitutional variants of the Zn{sub 11}Rh{sub 18}B{sub 8} structure type, space group P4/mbm (No. 127) and contain a “scaffold” structural unit (M-ladders interacting with M/Ti-chains) as well as isolated M/Ti-chains. According to DFT calculations, the Ru–X (X=B, Ti, Ti/M) bonding interactions are nearly constant throughout the series and responsible for the structural stability of these phases, whereas the M–M and Ru–M interactions vary significantly with varying valence electron count. Furthermore, density of states (DOS) analyses predict the phases with M=Mn and Ni to develop a total magnetic moment but not the M=Co phase. Susceptibility measurements confirm the Co phase to be paramagnetic and the Mn Phase orders ferrimagnetically below 120 K and thus develops a magnetic moment, as predicted. - Graphical abstract: The crystal structures of the new phases (M=Cr, Mn, Co, Ni) are confirmed by Rietveld refinement of powder diffraction data and single crystal X-ray diffractionmore » (for M=Co) to contain beside the M-ladder also M/Ti-chains. Similar to the series Ti{sub 9}M{sub 2}Ru{sub 18}B{sub 8}, the crystal structure of the new phases are mainly stabilized by the heteroatomic Ru–B and Ru–Ti bonds that remain nearly constant throughout the series, whereas the M-containing bonds vary significantly with varying valence electron count. An experimental finding confirmed and even extended by COHP bonding analyses. In addition, the DOS analyses of the M-elements reveal the development of magnetic moments for the M=Mn, Ni cases but not for M=Co. Indeed, Ti{sub 8}Co{sub 3}Ru{sub 18}B{sub 8} was found experimentally to be a paramagnet and ferrimagnetic ordering below 120 K is found for M=Mn. Highlights: • Synthesis of the valence electron controlled borides Ti{sub 9−x}M{sub 2+x}Ru{sub 18}B{sub 8} (M=Cr–Ni). • Prediction of magnetic properties of Ti{sub 9−x}M{sub 2+x}Ru{sub 18}B{sub 8}. • Ferrimagnetic ordering found in Ti{sub 8}M{sub 3}Ru{sub 18}B{sub 8}. • Chemical bonding studied by density functional theory.« less

A new structure type has been discovered in the system Sc–Fe–Ge–Sn by employing Sn as a flux medium. According to single crystal X-ray diffraction, the new structure has a composition of Sc4Fe5Ge6.10(3)Sn1.47(2) and crystallizes in the space group Immm (No. 71, oI144) with lattice parameters of a = 5.230(1) A, b = 13.467(3) A, and c = 30.003(6) A. The structure is composed of square anti-prismatic clusters that are condensed into zig-zag chains along the [0 1 0] direction. These chains are further condensed through a split Sn/Ge position, forming a three-dimensional network. Magnetization measurements indicate an antiferromagnetic phase transition near 240 K. Electronic structure calculations identified the most favorable bonding network in this new system. Using crystal orbital Hamilton population (COHP) curves and their integrated values (ICOHP), a polar intermetallic bonding network involving Sc–Ge as well as Fe–Sn and Fe–Ge contacts can be assigned to this new structure type.

Polycrystalline samples of two complex intermetallic borides Zr 2 Fe 1− δ Ru 5+ δ B 2 and Zr 2 Fe 1− δ (Ru 1− x Rh x ) 5+ δ B 2 ( δ =ca. 0.10; x =0.20) were synthesized by high-temperature methods and characterized by single-crystal X-ray diffraction, energy dispersive spectroscopy, and magnetization measurements. Both structures are variants of Sc 2 Fe(Ru 1− x Rh x ) 5 B 2 and crystallize in the space group P 4/ mbm (no. 127) with the Ti 3 Co 5 B 2 -type structure. These structures contain single-atom, Fe-rich Fe/Ru or Fe/Ru/Rh chains along the c -axis with an interatomic metal-metal distance of 3.078(1) A, a feature which makes them viable for possible low-dimensional temperature-dependent magnetic behavior. Magnetization measurements indicated weak ferrimagnetic ordering with ordering temperatures ca. 230 K for both specimens. Tight-binding electronic structure calculations on a model “Zr 2 FeRu 5 B 2 ” using LDA yielded a narrow peak at the Fermi level assigned to Fe–Fe antibonding interactions along the c -axis, a result that indicates an electronic instability toward ferromagnetic coupling along these chains. Spin-polarized calculations of various magnetic models were examined to identify possible magnetic ordering within and between the single-atom, Fe-rich chains.

The synthesis of persistent luminescent, monoclinic SrAl2O4:Eu2+,Dy3+ traditionally employs high temperature solid state methods, which tends to generate large particles and agglomerates (>15 μm). Alternatively, soft chemical synthetic routes are conducive to forming significantly smaller particles of SrAl2O4:Eu2+,Dy3+; unfortunately, many of the reported routes lead to impure products, including the presence of the hexagonal SrAl2O4:Eu2+,Dy3+ polymorph and Sr4Al14O25:Eu2+,Dy3+. Here, the combination of a solution-based reverse micelle microemulusion synthesis route combined with rapid microwave-assisted heating is shown to produce nearly phase pure monoclinic SrAl2O4:Eu2+,Dy3+ with a ≈70% smaller equivalent spherical diameter (4.2 μm) compared to the all solid state prepared materials (14.3 μm). Optical characterization including photon excitation, photon emission, persistent luminescent lifetime, and thermoluminescence measurements support that the optical properties remain almost unchanged, regardless of synthetic route. These results validate that monoclinic SrAl2O4:Eu2+,Dy3+ produced using this pathway is viable as an alternative to the all solid state-prepared materials, with the added advantage of significantly smaller particles that may be desirable when architecting new potential applications.

The electronic and magnetic structures of the tetragonal Cu2Sb-type 3d metal arsenides (M2As, M = Cr, Mn, Fe) were examined using density functional theory to identify chemical influences on their respective patterns of magnetic order. Each compound adopts a different antiferromagnetic (AFM) ordering of local moments associated with the 3d metal sites, but every one involves a doubled crystallographic c-axis. These AFM ordering patterns are rationalized by the results of VASP calculations on several magnetically ordered models using a × a × 2c supercell. Effective exchange parameters obtained from SPRKKR calculations indicate that both direct and indirect exchange couplings play essential roles in understanding the different magnetic orderings observed. The nature of nearest-neighbor direct exchange couplings, that is, either ferromagnetic (FM) or AFM, were predicted by analysis of the corresponding crystal orbital Hamilton population (COHP) curves obtained by TB-LMTO calculations. Interestingly, the magnetic structures of Fe2As and Mn2As show tetragonal symmetry, but a magnetostrictive tetragonal-to-orthorhombic distortion could occur in Cr2As through AFM Cr1-Cr2 coupling between symmetry inequivalent Cr atoms along the a-axis, but FM coupling along the b-axis. A LSDA+U approach is required to achieve magnetic moment values for Mn2As in better agreement with experimental values, although computations always predict the moment at the M1 site to be lower than that at the M2 site. Finally, a rigid-band model applied to the calculated DOS curve of Mn2As correctly assesses the magnetic ordering patterns in Cr2As and Fe2As.

A theoretical investigation of the relationship between chemical composition and electronic structure was performed on the nonstoichiometric iron sulfide, mackinawite (Fe(1+x)S), which is isostructural and isoelectronic with the superconducting Fe(1+x)Se and Fe(1+x)(Te(1-y)Se(y)) phases. Even though Fe(1+x)S has not been measured for superconductivity, the effects of stoichiometry on transport properties and electronic structure in all of these iron-excess chalcogenide compounds has been largely overlooked. In mackinawite, the amount of Fe that has been reported ranges from a large excess, Fe(1.15)S, to nearly stoichiometric, Fe(1.00(7))S. Here, we analyze, for the first time, the electronic structure of Fe(1+x)S to justify these nonstoichiometric phases. First principles electronic structure calculations using supercells of Fe(1+x)S yield a wide range of energetically favorable compositions (0x0.30). The incorporation of interstitial Fe atoms originates from a delicate balance between the Madelung energy and the occupation of Fe-S and Fe-Fe antibonding orbitals. A theoretical assessment of various magnetic structures for "FeS" and Fe(1.06)S indicate that striped magnetic ordering along [110] is the lowest energy structure and the interstitial Fe affects the values of moments in the square planes as a function of distance. Moreover, the formation of the magnetic moment is dependent on the unit cell volume, thus relating it to composition. Finally, changes in the composition cause a modification of the Fermi surface and ultimately the loss of a nested vector.

The development of superhard materials is focused on two very different classes of compounds. The first contains only light, inexpensive main group elements and requires high pressures and temperatures for preparation whereas the second class combines a transition metal with light main group elements and in general tends to only need high reaction temperatures. Although the preparation conditions are simpler, the second class of compounds suffers from the transition metals used being expensive and exceedingly scarce. Thus, in the search for novel superhard compounds, synthetic accessibility, resource considerations, and material response must be balanced. The research presented here develops high-information density plots drawn from high-throughput first-principle calculations and data mining to reveal the optimal composition space to synthesize new materials. This contribution includes analysis of the experimentally known Vickers hardness for materials as well as screening over 1100 compounds from first-principle calculations to predict their intrinsic hardness. Both data sets are analyzed not only for their mechanical performance but also the compositional scarcity, and Herfindahl-Hirschman index is calculated. Following this methodology, it is possible to ensure targeted materials are not only sustainable and accessible but that they will also have superb mechanical response.