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 treatment of data, feature engineering, model training, validation, evaluation comparison, popular repositories data benchmarking sets, architecture sharing, finally publication. In addition, we include interactive Jupyter notebooks with example Python code to demonstrate some concepts, workflows,...

A machine-learning model has been trained to discover Heusler compounds, which are intermetallics exhibiting diverse physical properties attractive for applications in thermoelectric and spintronic materials. Improving these requires knowledge of crystal structures, occur three subtle variations (Heusler, inverse Heusler, CsCl-type structures) that difficult, at times impossible, distinguish by diffraction techniques. Compared alternative approaches, this discovery engine performs...

In the pursuit of materials with exceptional mechanical properties, a machine-learning model is developed to direct synthetic efforts toward compounds high hardness by predicting elastic moduli as proxy. This approach screens 118 287 compiled in crystal structure databases for highest bulk and shear determined support vector machine regression. Following these models, ternary rhenium tungsten carbide quaternary molybdenum borocarbide are selected synthesized at ambient pressure....

Abstract Rare-earth substituted inorganic phosphors are critical for solid state lighting. New 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 phosphor host crystal structure’s Debye temperature, which is proxy photoluminescent quantum yield, with high-throughput density functional theory calculations evaluate band gap. This...

Most discoveries in materials science have been made empirically, typically through one-variable-at-a-time (Edisonian) experimentation. The characteristics of materials-based systems are, however, neither simple nor uncorrelated. In a device such as an organic photovoltaic, for example, the level complexity is high due to sheer number components and processing conditions, thus, changing one variable can multiple unforeseen effects their interconnectivity. Design Experiments (DoE) ideally...

The experimental search for new thermoelectric materials remains largely confined to a limited set of successful chemical and structural families, such as chalcogenides, skutterudites, Zintl phases. In principle, computational tools density functional theory (DFT) offer the possibility rationally guiding synthesis efforts toward very different chemistries. However, in practice, predicting properties from first principles challenging endeavor [J. Carrete et al., Phys. Rev. X 4, 011019...

ConspectusIntermetallic compounds are bestowed by diverse compositions, complex structures, and useful properties for many materials applications. How metallic elements react to form these what structures they adopt remain challenging questions that defy predictability. Traditional approaches offer some rational strategies prepare specific classes of intermetallics, such as targeting members within a modular homologous series, manipulating building blocks assemble new filling interstitial...

The rapidly growing interest in machine learning (ML) for materials discovery has resulted a large body of published work. However, only small fraction these publications includes confirmation ML predictions, either via experiment or physics-based simulations. In this review, we first identify the core components common to informatics pipelines, such as training data, choice algorithm, and measurement model performance. Then discuss some prominent examples validated ML-driven across wide...

Partial least-squares discriminant analysis (PLS-DA) and support vector machine (SVM) techniques were applied to develop a crystal structure predictor for binary AB compounds. Models trained validated on the basis of classification 706 compounds adopting seven most common types (CsCl, NaCl, ZnS, CuAu, TlI, β-FeB, NiAs), through data extracted from Pearson’s Crystal Data ASM Alloy Phase Diagram Database. Out 56 initial variables (descriptors based elemental properties only), 31 selected in as...

Efficient white-light-emitting single-material sources are ideal for sustainable lighting applications. Though layered hybrid lead–halide perovskite materials have demonstrated attractive broad-band white-light emission properties, they pose a serious long-term environmental and health risk as contain lead (Pb2+) readily soluble in water. Recently, lead-free halide double (HDP) with generic formula A(I)2B′(III)B″(I)X6 (where A B cations X is ion) improved photoluminescence quantum yields...

Abstract In normal daily activity, ligaments are probably subjected to repeated loading rather than deformation. The viscoelastic response is creep; this effect has significance for ligament reconstructions, which potentially “stretch out” over time. However, most experimental studies have examined the deformation, stress relaxation. We hypothesized that creep of a could be predicted from its stress‐relaxation behaviour. Left and right medial collateral eight skeletally mature rabbits were...

A method to predict the crystal structure of equiatomic ternary compositions based only on constituent elements was developed using cluster resolution feature selection (CR-FS) and support vector machine (SVM) classification. The supervised machine-learning model first trained with 1037 individual compounds that adopt most populated 1:1:1 types (TiNiSi-, ZrNiAl-, PbFCl-, LiGaGe-, YPtAs-, UGeTe-, LaPtSi-type) then validated an additional 519 compounds. CR-FS algorithm improves class...

Silicon nanoparticles (SiNPs) are biologically compatible, metal-free quantum dots that exhibit size and surface tailorable photoluminescence. The nanostructure of these materials influences their optical, chemical, material properties hence plays an important role in future-generation applications sensors, battery electrodes, optical materials, contrast agents, among others. In this work, we employ a complement methods including X-ray photoelectron spectroscopy (XPS), bright-field...

Abstract An ensemble machine‐learning method is demonstrated to be capable of finding superhard materials by directly predicting the load‐dependent Vickers hardness based only on chemical composition. A total 1062 experimentally measured data are extracted from literature and used train a supervised algorithm utilizing boosting, achieving excellent accuracy ( R 2 = 0.97). This new model then tested synthesizing measuring several unreported disilicides analyzing predicted classic materials....

Materials informatics employs data-driven approaches for analysis and discovery of materials. Features also referred to as descriptors are essential in generating reliable accurate machine-learning models. While general data can be obtained through public commercial sources, features must tailored specific applications. Common featurizers suitable generic chemical problems may not effective features-property mapping solid-state materials with ML Here, we have assembled the Oliynyk property...

The billions of tons mineral dust released into the atmosphere each year provide an important surface for reaction with gas-phase pollutants. These reactions, which are often enhanced in presence light, can change both composition and properties itself. Because contains titanium-rich grains, studies photochemistry have largely employed commercial titanium dioxide as a proxy its photochemically active fraction; to date, however, validity this model system has not been empirically determined....

An innovative application of metal–organic frameworks (MOFs) is in biomedical materials. To treat bone demineralization, which a hallmark osteoporosis, biocompatible MOFs (bioMOFs) have been proposed various components, such as alkaline-earth cations and bisphosphonate molecules, can be delivered to maintain normal density. Multicomponent bioMOFs that release several components simultaneously at controlled rate thus offer an attractive solution. We report two new bioMOFs, comprising...

"Two quantum dots, both alike in composition, but differing structure, where we lay our scene, From broader classes, to bring deeper understanding, the crystalline core that drives dot's sheen." In this contribution examine two families of silicon dots (SiQDs) mind Capulets and Montagues Shakespeare's Romeo Juliet because their stark similarities differences. SiQDs are highly luminescent, heavy-metal-free, based upon earth-abundant elements. As such, they have attracted attention for...

This Editorial is intended for materials scientists interested in performing machine learning-centered research. We cover broad guidelines and best practices regarding the obtaining treatment of data, feature engineering, model training, validation, evaluation comparison, popular repositories data benchmarking datasets, architecture sharing, finally publication.In addition, we include interactive Jupyter notebooks with example Python code to demonstrate some concepts, workflows, discussed....

Heusler compounds form a diverse group of intermetallic materials encompassing many compositions and structures derived from cubic prototypes, exhibiting complicated types disorder phenomena. In particular, preparing solid solutions between half-Heusler ABC full-Heusler AB2C offers means to control physical properties. However, as is typical in discovery, they represent only small fraction possible compounds. To address this problem unbalanced data sets, machine-learning model was developed...