The emergence of methyl-ammonium lead halide (MAPbX3) perovskites motivates the identification of unique properties giving rise to exceptional bulk transport properties, and identifying future materials with similar properties. Here, we propose that this “defect tolerance” emerges from fundamental electronic-structure properties, including the orbital character of the conduction and valence band extrema, the charge-carrier effective masses, and the static dielectric constant. We use MaterialsProject.org searches and detailed electronic-structure calculations to demonstrate these properties in other materials than MAPbX3. This framework of materials discovery may be applied more broadly, to accelerate discovery of new semiconductors based on emerging understanding of recent successes.

Recently, we reported on the facile synthesis of a number of two-dimensional early transition metal carbides and nitrides, derived from the MAX phases, that we labeled MXenes. Herein, we report on the electronic and elastic properties—investigated by first principles calculations utilizing the generalized gradient approximation within the density functional theory—of the following two-dimensional transition metal carbides: Ti2C, Ti3C2, Ti4C3, V2C, Cr2C, Zr2C, Hf2C, and Ta2C, Ta3C2, and Ta4C3. Similar to the MAX phases, the MXenes are found to be metallic and possess high elastic moduli when stretched along the basal planes.

The ferroelectricity in fluorite-structure oxides such as hafnia and zirconia has attracted increasing interest since 2011. They have various advantages such as Si-based complementary metal oxide semiconductor-compatibility, matured deposition techniques, a low dielectric constant and the resulting decreased depolarization field, and stronger resistance to hydrogen annealing. However, the wake-up effect, imprint, and insufficient endurance are remaining reliability issues. Therefore, this paper reviews two major aspects: the advantages of fluorite-structure ferroelectrics for memory applications are reviewed from a material's point of view, and the critical issues of wake-up effect and insufficient endurance are examined, and potential solutions are subsequently discussed.

With multiple elements mixed at equal or near-equal molar ratios, the emerging, high-entropy alloys (HEAs), also named multi-principal elements alloys (MEAs), have posed tremendous challenges to materials scientists and physicists, e.g., how to predict high-entropy phase formation and design alloys. In this paper, we propose some guidelines in predicting phase formation, using thermodynamic and topological parameters of the constituent elements. This guideline together with the existing ones will pave the way toward the composition design of MEAs and HEAs, as well as property optimization based on the composition–structure–property relationship.

The remarkable optoelectronic and especially photovoltaic performance of hybrid organic–inorganic perovskite (HOIP) materials drives efforts to connect materials properties to this performance. From nano-indentation experiments on solution-grown single crystals we obtain elastic modulus and nano-hardness values of APbX3 (A = Cs, CH3NH3; X = I, Br). The Young's moduli are ~14, 19.5, and 16 GPa, for CH3NH3PbI3, CH3NH3PbBr3, and CsPbBr3, respectively, lending credence to theoretically calculated values. We discuss the possible relevance of our results to suggested “self-healing”, ion diffusion, and ease of manufacturing. Using our results, together with literature data on elastic moduli, we classified HOIPs amongst the relevant material groups, based on their elastomechanical properties.

Gelatin-based hydrogels derived from hydrolysis of collagen have been extensively used in pharmaceutical and medical applications because of their biocompatibility and biodegradability. For example, gelatin-based hydrogels are finding use in drug delivery and tissue engineering because they are able to promote cell adhesion and proliferation. In addition, these hydrogels can be used as wound dressings due to their attractive fluid absorbance properties. Manufacturing technologies such as ultraviolet stereolithography and two-photon polymerization can be used to prepare structures containing photosensitive gelatin-based hydrogels. This review describes the preparation of gelatin-based hydrogels and use of these materials for biomedical applications.

Machine learning (ML) has been perceived as a promising tool for the design and discovery of novel materials for a broad range of applications. In this prospective paper, we summarize recent progress in the applications of ML to composite materials modeling and design. An overview of how different types of ML algorithms can be applied to accelerate composite research is presented. This framework is envisioned to revolutionize approaches to design and optimize composites for the next generation of materials with unprecedented properties.

The authors showcase the potential of symbolic regression as an analytic method for use in materials research. First, the authors briefly describe the current state-of-the-art method, genetic programming-based symbolic regression (GPSR), and recent advances in symbolic regression techniques. Next, the authors discuss industrial applications of symbolic regression and its potential applications in materials science. The authors then present two GPSR use-cases: formulating a transformation kinetics law and showing the learning scheme discovers the well-known Johnson–Mehl–Avrami–Kolmogorov form, and learning the Landau free energy functional form for the displacive tilt transition in perovskite LaNiO3. Finally, the authors propose that symbolic regression techniques should be considered by materials scientists as an alternative to other machine learning-based regression models for learning from data.

The growing application of data-driven analytics in materials science has led to the rise of materials informatics. Within the arena of data analytics, deep learning has emerged as a game-changing technique in the last few years, enabling numerous real-world applications, such as self-driving cars. In this paper, the authors present an overview of deep learning, its advantages, challenges, and recent applications on different types of materials data. The increasingly availability of materials databases and big data in general, along with groundbreaking advances in deep learning offers a lot of promise to accelerate the discovery, design, and deployment of next-generation materials.

Over the past three decades, the combination of inorganic-nanoparticles and organic-polymers has led to a wide variety of advanced materials, including polymer nanocomposites (PNCs). Recently, synthetic innovations for attaching polymers to nanoparticles to create “hairy nanoparticles” (HNPs) has expanded opportunities in this field. In addition to nanoparticle compatibilization for traditional particle–matrix blending, neat-HNPs afford one-component hybrids, both in composition and properties, which avoids issues of mixing that plague traditional PNCs. Continuous improvements in purity, scalability, and theoretical foundations of structure–performance relationships are critical to achieving design control of neat-HNPs necessary for future applications, ranging from optical, energy, and sensor devices to lubricants, green-bodies, and structures.

Some of the stable isotopes of silicon and carbon have zero nuclear spin, whereas many of the other elements that constitute semiconductors consist entirely of stable isotopes that have nuclear spins. Silicon and diamond crystals composed of nuclear-spin-free stable isotopes (28Si, 30Si, or 12C) are considered to be ideal host matrixes to place spin quantum bits (qubits) for quantum-computing and -sensing applications, because their coherent properties are not disrupted thanks to the absence of host nuclear spins. The present paper describes the state-of-the-art and future perspective of silicon and diamond isotope engineering for development of quantum information-processing devices.

Hybrid inorganic–organic perovskites have emerged over the last 5 years as a promising class of materials for optoelectronic applications. Most notably, their solar cells have achieved power conversion efficiencies above 20% in an unprecedented timeframe; however, many fundamental questions still remain about these materials. This Prospective Article reviews the procedures used to deposit hybrid perovskites and describes the resulting crystallographic and morphological structures. It further details the electrical and optical properties of perovskites and then concludes by highlighting a number of potential applications and the materials challenges that must be overcome before they can be realized.

The hybrid halide perovskites combine the low-cost processing characteristics of organic materials with the performance factors of inorganic compounds. Recently the power conversion efficiencies of perovskite photovoltaic solar cells have reached a respective value of ~20%. The charge transport properties were indirectly approximated in these compounds because of lack of available field-effect transistors (FETs). Here we report the fabrication and room-temperature operation of FETs based on the hybrid perovskites. We obtained balanced electron and hole transport with mobilities of ~1 cm2/Vs. We also found that the yield, as well as the operational and environmental stability of the fabricated transistors is limited.

The use of statistical/machine learning (ML) approaches to materials science is experiencing explosive growth. Here, we review recent work focusing on the generation and application of libraries from both experiment and theoretical tools. The library data enables classical correlative ML and also opens the pathway for exploration of underlying causative physical behaviors. We highlight key advances facilitated by this approach and illustrate how modeling, macroscopic experiments, and imaging can be combined to accelerate the understanding and development of new materials systems. These developments point toward a data-driven future wherein knowledge can be aggregated and synthesized, accelerating the advancement of materials science.

Facilitating the application of machine learning (ML) to materials science problems requires enhancing the data ecosystem to enable discovery and collection of data from many sources, automated dissemination of new data across the ecosystem, and the connecting of data with materials-specific ML models. Here, we present two projects, the Materials Data Facility (MDF) and the Data and Learning Hub for Science (DLHub), that address these needs. We use examples to show how MDF and DLHub capabilities can be leveraged to link data with ML models and how users can access those capabilities through web and programmatic interfaces.

We report the synthesis of a direct gap semiconductor, ZnSnN2, by a plasma-assisted vapor–liquid–solid technique. Powder X-ray diffraction measurements of polycrystalline material yielded lattice parameters in good agreement with predicted values. Photoluminescence efficiency at room temperature was observed to be independent of excitation intensity between 103 and 108 W/cm2. The band gap was measured by photoluminescence excitation spectroscopy to be 1.7 ± 0.1 eV. The range of direct band gaps for the Zn(Si,Ge,Sn)N2 alloys is now predicted to extend from 4.5 to 1.7 eV, opening up this little-studied family of materials to a host of important applications.

Besides graphite and diamond, the solid allotropes of carbon in sp2 and sp3 hybridization, the possible existence of a third allotrope based on the sp-carbon linear chain, the carbyne, has stimulated researchers for a long time. The advent of fullerenes, nanotubes, and graphene has opened new opportunities and nurtured the interest in novel carbon allotropes, including linear structures. The efforts made in this direction produced a number of interesting sp-hybridized carbon molecules and nanostructures in the form of carbon-atom wires. Here we discuss some of the new perspectives opened by the recent advancements in the research on sp-carbon systems.

Machine-learning (ML) approaches have proven to be of great utility in modern materials innovation pipelines. Generally, ML models are trained on predetermined past data and then used to make predictions for new test cases. Active-learning, however, is a paradigm in which ML models can direct the learning process itself through providing dynamic suggestions/queries for the “next-best experiment.” In this work, the authors demonstrate how an active-learning framework can aid in the discovery of polymers possessing high glass transition temperatures (Tg). Starting from an initial small dataset of polymer Tg measurements, the authors use Gaussian process regression in conjunction with an active-learning framework to iteratively add Tg measurements of candidate polymers to the training dataset. The active-learning framework employs one of three decision making strategies (exploitation, exploration, or balanced exploitation/exploration) for selection of the “next-best experiment.” The active-learning workflow terminates once 10 polymers possessing a Tg greater than a certain threshold temperature are selected. The authors statistically benchmark the performance of the aforementioned three strategies (against a random selection approach) with respect to the discovery of high-Tg polymers for this particular demonstrative materials design challenge.

Electrochemical technologies promise to provide the means for electrical energy storage of electricity generated from wind, solar, or nuclear energies. The challenge is to provide this storage in rechargeable batteries or clean fuels at a cost that is competitive with fossil fuels for replacement: (1) of vehicles powered by the internal combustion engine by electric vehicles and (2) of centralized power plants using intermittent electricity generated by wind and solar energy or constant electricity from a nuclear power plant, all serving a variable demand. This perspective outlines existing and possible lines of materials research for the development of rechargeable batteries or the production of clean fuels within the constraints of electrochemical technology.

Due to the special crystal structures and electron configurations, high-entropy alloys (HEAs) are expected to have favorable activities for electrocatalytic reactions. In this paper, a set of oxygen evolution reaction (OER) criteria are applied for the HEA-based electrocatalyst design. Specifically, FeNiMnCrCu HEA is predicted to have a better OER performance than the baseline FeCoNiCrAl HEA. To demonstrate this design approach, both FeNiMnCrCu and FeCoNiCrAl samples are prepared and tested. Their crystal structures and electrocatalytic performance are examined. This paper demonstrates the potential of using finely tuned HEAs for OER applications.