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DEED captures the balance between entropy gains and costs, allowing the correct classification of functional synthesizability of multicomponent ceramics, regardless of chemistry and structure, and provides an array of potential new candidates, ripe for experimental discoveries.

Topological insulators exhibit intriguing electronic properties that originate from protected metallic states on their surface. Experimental studies so far are based on a limited number of materials. A high-throughput approach now shows how to search for topological insulators in a variety of unexplored classes of materials.

High-throughput computational approaches combining thermodynamic and electronic-structure methods with data mining and database construction are increasingly used to analyse huge amounts of data for the discovery and design of new materials. This Review provides an overall perspective of the field for a broad range of materials, and discusses upcoming challenges and opportunities.

The study of the electronic, thermal and optical properties of dysprosium-doped cadmium oxide reveals high electron mobility, rendering the material suitable for plasmonic applications in the mid-infrared region.

The composition of oxide compounds controls many of their properties and electronic phases. Here, the authors show that entropy and configurational disorder can stabilize new phases of oxides, potentially enabling a better engineering of their properties.

It is crucial yet challenging to predict good glass formers in search of new metallic materials for industrial use. Here, Perim et al. develop computational descriptors based on the density of local crystalline metastable phases to predict glass forming ability and find more promising materials.

Tunable plasmonic materials capable of surviving harsh environments are critical for advanced applications. Here, the authors report that some high-entropy transition-metal carbides can satisfy the requirements.

The contribution of vibrations to the stability of high-entropy ceramics is still controversial. Here the authors computationally integrate disorder parameterization, phonon modelling, and thermodynamic characterization to investigate the role of vibrations to the stability of high-entropy carbides.

Machine learning methods can be useful for materials discovery; however certain properties remain difficult to predict. Here, the authors present a universal machine learning approach for modelling the properties of inorganic crystals, which is validated for eight electronic and thermomechanical properties.

The overwhelming number of possible high-entropy materials represents a big challenge for predicting their existence. Here, the authors introduce an entropy-forming-ability descriptor capturing the synthesizability of these systems, and apply the model to the discovery of new refractory metal carbides.

Machine learning driven research holds big promise towards accelerating materials’ discovery. Here the authors demonstrate CAMEO, which integrates active learning Bayesian optimization with practical experiments execution, for the discovery of new phase- change materials using X-ray diffraction experiments.

It is shown that the large thermoelectric capability of CoSb₃ skutterudite can be associated with a secondary conduction band with high valley degeneracy, which can converge with the light conduction band at high temperatures.

Hypersonic vehicles experience extreme temperatures, high heat fluxes, and aggressive oxidizing environments. Here, the authors highlight key materials design principles for critical vehicle areas and strategies for advancing laboratory-scale materials to flight-ready components.

Machine learning is enabling a metallurgical renaissance. This Review discusses recent progress in representations, descriptors and interatomic potentials, overviewing metallic glasses, high-entropy alloys, superalloys and shape-memory alloys, magnets and catalysts, and the prediction of mechanical and thermal properties.

The valuable combination of disorder and non-metallic bonding gives rise to high-entropy ceramics. This Review explores the structures and chemistries of these versatile materials, and their applications in catalysis, water splitting, energy storage, thermoelectricity and thermal, environmental and wear protection.

The expansive production of data in materials science, their widespread sharing and repurposing requires educated support and stewardship. In order to ensure that this need helps rather than hinders scientific work, the implementation of the FAIR-data principles (Findable, Accessible, Interoperable, and Reusable) must not be too narrow. Besides, the wider materials-science community ought to agree on the strategies to tackle the challenges that are specific to its data, both from computations and experiments. In this paper, we present the result of the discussions held at the workshop on “Shared Metadata and Data Formats for Big-Data Driven Materials Science”. We start from an operative definition of metadata, and the features that  a FAIR-compliant metadata schema should have. We will mainly focus on computational materials-science data and propose a constructive approach for the FAIRification of the (meta)data related to ground-state and excited-states calculations, potential-energy sampling, and generalized workflows. Finally, challenges with the FAIRification of experimental (meta)data and materials-science ontologies are presented together with an outlook of how to meet them.

Bulk metallic glasses typically display limited ductility. Here, straining a bulk metallic glass during cooling from the supercooled region is shown to enhance bending ductility, attributed to the structure being pulled up the potential energy landscape.