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Superhard materials are among the most scarce functional inorganic solids in existence. Indeed, recent research suggested that less than 0.1% of all known materials are likely to have a Vickers hardness ≥40 GPa. Here, an anomaly detection framework is created to treat these materials as rare occurrences by encoding and reconstructing the input composition and crystal structure information without supervision. The resulting model can quantitatively identify outliers from "normal" behaving materials, leading to the discovery of materials with exceptional properties such as a superhard response. Moreover, examining the difference between the encoded and decoded crystal structure provides fundamental insights into the crystal-chemical origin of hardness. The presented methodology is ultimately generalizable, enabling the design of other outlier materials with rare and unexpected physical properties.

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.

(Ba1−xSrx)0.98Eu0.02ScO2F produces a tunable emission from cyan to green upon Sr2+ substitution. Ab initio molecular dynamics simulations revealed that Sr2+ weakens the local rigidity lowering the efficiency and thermal stability of the phosphors.

Electron density, Fermi level position, and chemical bonding are essential markers when developing high hardness complex borides.

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.

Broadband near-infrared (NIR) emitting materials are in great demand as next-generation smart NIR light sources. In this work, a Cr3+-substituted phosphor capable of efficiently converting visible to NIR light is developed through the solid solution, Ga2-xInxO3:Cr3+ (0 ≤ x ≤ 0.5). The compounds were prepared using high-temperature solid-state synthesis, and the crystal and electronic structure, morphology, site preference, and photoluminescence properties are studied. The photoluminescence results demonstrate a high quantum yield (88%) and impressive absorption efficiency (50%) when x = 0.4. The NIR emission is tunable across a wide range (713-820 nm) depending on the value of x. Moreover, fabricating a prototype of a phosphor-converted NIR light-emitting diode (LED) device using 450 nm LED and the [(Ga1.57Cr0.03)In0.4]O3 phosphor showed an output power that reached 40.4 mW with a photoelectric conversion efficiency of 25% driven by a current of 60 mA, while the resulting device was able to identify damaged produce that was undetectable using visible light. These results demonstrate the outstanding potential of this phosphor for NIR LED imaging applications.

Single crystals of three new compounds, Na

The proliferation of energy-efficient light-emitting diode (LED) lighting has resulted in continued exposure to blue light, which has been linked to cataract formation, circadian disruption, and mood disorders. Blue light can be readily minimized in pursuit of "human-centric" lighting using a violet LED chip (λem ≈ 405 nm) downconverted by red, green, and blue-emitting phosphors. However, few phosphors efficiently convert violet light to blue light. This work reports a new phosphor that meets this demand. Na2MgPO4F:Eu2+ can be excited by a violet LED yielding an efficient, bright blue emission. The material also shows zero thermal quenching and has outstanding chromatic stability. The chemical robustness of the phosphor was also confirmed through prolonged exposure to water and high temperatures. A prototype device using a 405 nm LED, Na2MgPO4F:Eu2+, and a green and red-emitting phosphor produces a warm white light with a higher color rendering index than a commercially purchased LED light bulb while significantly reducing the blue component. These results demonstrate the capability of Na2MgPO4F:Eu2+ as a next-generation phosphor capable of advancing human-centric lighting.

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.

Three new members in the Bi

The host structure of the new phosphor Sr6(BO3)3BN2:Eu2+ crystallizes tetragonally (Sr6(BO3)3BN2, P42/mbc, Z = 8, a = 12.8173(3) A, b = 14.0740(5) A, Rint = 0.037, R1 = 0.052, and wR2 = 0.121) and ...

The dual emission produced from Mn2+ when codoped with rare earth ions like Eu2+ or Ce3+ in inorganic compounds makes these materials attractive as efficient, color-tunable phosphors for warm-white...

Phosphor-converted light emitting diodes (LEDs) are a highly efficient form of solid-state lighting. A key performance metric of a phosphor is its thermal quenching (TQ), which is the percentage lo...

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...

Borides containing 4d or 5d transition metals are among the most common types of high hardness materials because of the high valence electron density of the metals combined with short, covalent mai...

Complex alkaline earth silicates have been extensively studied as rare-earth substituted phosphor hosts for use in solid-state lighting. One of the biggest challenges facing the development of new phosphors is understanding the relationship between the observed optical properties and the crystal structure. Fortunately, recent improvements in characterization techniques combined with advances in computational methodologies provide the research tools necessary to conduct a comprehensive analysis of these systems. In this work, a new Ce3+ substituted phosphor is developed using Ba5Si8O21 as the host crystal structure. The compound is evaluated using a combination of experimental and computational methods and shows Ba5Si8O21:Ce3+ adopts a monoclinic crystal structure that was confirmed through Rietveld refinement of high-resolution synchrotron powder X-ray diffraction data. Photoluminescence spectroscopy reveals a broad-band blue emission centered at ∼440 nm with an absolute quantum yield of ∼45% under ultraviolet light excitation (λex = 340 nm). This phosphor also shows a minimal chromaticity-drift but with moderate thermal quenching of the emission peak at elevated temperatures. The modest optical response of this phase is believed to stem from a combination of intrinsic structural complexity and the formation of defects because of the aliovalent rare-earth substitution. Finally, computational modeling provides essential insight into the site preference and energy level distribution of Ce3+ in this compound. These results highlight the importance of using experiment and computation in tandem to interpret the relationship between observed optical properties and the crystal structures of all rare-earth substituted complex phosphors.

There is a significant need to identify cyan-emitting phosphors capable of filling the “cyan-gap” (480–520 nm) in full-visible-spectrum phosphor-converted white light-emitting diodes (pc-wLEDs). Here, a new broadband cyan-emitting phosphor that enables addressing of this challenge is reported. The compound, Ba2CaB2Si4O14:Ce3+, presents a bright cyan emission peaking at 478 nm with a large full width at half maximum of 142 nm (6053 cm−1), and minimal thermal quenching. The photoluminescence properties originate from Ce3+ residing at two different crystallographic sites, a [BaO9] distorted elongated square pyramid and a [CaO6] trigonal prism. This combination results in an efficient, broad emission covering the blue to green region of the visible spectrum. Fabricating a simple dichromatic ultraviolet (λex = 370 nm) pumped pc-wLED using Ba2CaB2Si4O14:Ce3+ along with a commercially available red phosphor demonstrates full-visible-spectrum white light with high color rendering index (Ra > 90) and tunable correlated color temperature, showing the potential of this material for achieving high-quality LED-based lighting.

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 photoluminescence spectrum generated by an ordinary phosphor-converted white light-emitting diode (pc-wLED) that combines a blue LED chip with a yellow phosphor or a near-UV LED with red, green...

One of society’s grand challenges is to reduce energy usage in ways that are cost-effective, sustainable, and environmentally benign. Replacing incandescent and compact fluorescent light bulbs with...

In the race to develop new luminescent materials for the next generation of light-emitting-diode (LED)-based solid-state lighting and display applications, it is often forgotten that color theory and human perception should be some of the principal factors guiding materials design. In this Viewpoint, we explore some of the antiquated colorimetrics established originally for incandescent and fluorescent lighting and discuss how they are still widely applied in the literature today to interpret the color quality of luminescent materials, like inorganic phosphors and quantum dots, and to analyze prototype devices, despite their shortcomings. We then shift our analysis toward contemporary ideas in color theory that more accurately describe the color quality of modern LED light bulbs and flat-panel displays. Finally, the perspective examines the opportunities and challenges of applying these new concepts to guide the design of luminescent materials used in LED-based applications.

Downconverters, primarily inorganic phosphors, are critical components in white solid-state LED-based lighting and liquid crystal display backlights. Research efforts have led to a fundamental understanding of a downconverter's absorption, photoluminescence, and efficiency as a function of composition, structure, and processing conditions. However, considerably less work has focused on the reliability of phosphors once they are incorporated into LED packages. Solving these issues is often the final step before the commercialization of new materials, but the significant resources and time required to evaluate and mitigate materials failure are rarely discussed in the literature. In this Perspective, we discuss the need for conducting downconverter reliability testing and the potential of accelerating, screening, and understanding downconverter failure modes. Our focus highlights the mechanisms of failure and discusses how this influences materials selection and the design of different LED packages. We also stress the potential for accelerated reliability testing protocols and note the potential role first-principles calculations and data-driven models could play in establishing the compositional-processing trends for different aspects of downconverter reliability. We close with possible research directions that could improve downconverter reliability and emphasize the importance of assessing a material's (chemical) stability where multiple manufacturing and processing steps can dictate system performance.

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 concept of human-centric lighting stems from the evolution of sunlight’s intensity and color temperature throughout the course of a day. This natural progression of bright cold-white light during the middle of the day to a softer warm-white light in the evening stimulates intrinsic photosensitive retinal ganglion cells that control our circadian rhythm. The blue-hue of daylight activates these cells to produce dopamine and cortisol while suppressing melatonin, the sleep hormone, to keep humans awake and alert. The current generation of energy-efficient LED lights reproduce daylight by converting a blue-emitting LED into a broad-spectrum white light using inorganic phosphors. Unfortunately, the resulting intense blue-hue generated by cheap LED bulbs and the underlying blue light from even the most expensive bulbs have been shown to cause macular degeneration, cataract formation, mood disorders, and circadian disruption, resulting in insomnia and fatigue. This talk will investigate the production of a ‘human-centric’ light that minimizes blue light by using a violet LED chip and inorganic phosphors. We report a new phosphor, Na2MgPO4F:Eu2+, which can be readily excited by violet light to produce a bright blue emission. This material possesses all the necessary requirements for LED lighting, including a high quantum yield, thermally robust emission, and impressive chemical stability. Incorporating this material into a prototype device demonstrates our ability produce a warm-white light with a higher color rendering index than a commercially purchased LED light bulb while significantly reducing the blue component.

Assessing the hardness of structural materials at elevated temperatures is experimentally and computationally challenging, yet crucial for their success. In this work, a machine-learning method was developed to determine a material's temperature-dependent hardness based on its chemical composition and crystal structure. A total of 593 Vickers hardness data collected at various temperatures were extracted from the literature and used to train an extreme gradient boosting (XGBoost) machine-learning model. Applying a combination of composition descriptors and smooth overlap of atomic positions (SOAP) structural descriptors to represent these materials resulted in outstanding accuracy (R2 = 0.91; MAE = 2.52 GPa). The model's intrinsic variance was also measured by using a bootstrap aggregating (bagging) method, and the subsequent predictions showed strong agreement with the experimental data. The capability of the trained model was finally verified by demonstrating the model's ability to discriminate polymorphs, separate the properties of similar compositions, and reproduce the high-temperature hardness of several classic structural materials.

Broadband near-infrared (NIR) emitting materials are in great demand as next-generation smart NIR light sources. In this work, a Cr