MXenes are a large family of two-dimensional (2D) transition-metal carbides, nitrides, and carbonitrides. The MXene family has expanded since their original discovery in 2011, and has grown larger with the discovery of ordered double transition-metal (DTM) MXenes. These DTM MXenes differ from their counterpart mono-transition-metal (mono-M) MXenes, where two transition metals can occupy the metal sites. Ordered DTM MXenes are comprised of transition metals in either an in-plane or out-of-plane ordered structure. Additionally, some DTM MXenes are in the form of random solid solutions, which are defined by two randomly distributed transition metals throughout the 2D structure. Their different structures and array of transition-metal pairs provide the ability to tune DTM MXenes for specific optical, magnetic, electrochemical, thermoelectric, catalytic, or mechanical behavior. This degree of control over their composition and structure is unique in the field of 2D materials and offers a new avenue for application-driven design of functional nanomaterials. In this article, we review the synthesis, structure, and properties of DTM MXenes and provide an outlook for future research in this field.

Although halide perovskites (HaPs) are synthesized in ways that appear antithetical to those required for yielding high-quality semiconductors, the properties of the resulting materials imply, particularly for single crystals, ultralow densities of optoelectronically active defects. This article provides different views of this unusual behavior. We pose the question: Can present models of point defects in solids be used to interpret the experimental data and provide predictive power? The question arises because the measured ultralow densities refer to static defects using our present methods and models, while dynamic defect densities are ultrahigh, a result of the material being relatively soft, with a shallow electrostatic energy landscape, and with anharmonic lattice dynamics. All of these factors make the effects of dynamic defects on the materials’ optoelectronic properties minimal. We hope this article will stimulate discussions on the nontrivial question: Are HaPs, and especially the defects within them, business as usual?

Quantum computing, sensing, and communications are emerging technologies that may circumvent known limitations of their existing traditional counterparts. While the promises of these technologies are currently narrow in scope, it is possible that they will broadly impact our lives by revolutionizing the capabilities of data centers and medical diagnostics, for example. At the heart of these technologies is the use of a quantum object to contain information, called a quantum bit or qubit. Current realizations of qubits exist in a broad variety of material systems, including individual spins in semiconductors or insulators, superconducting circuits, and trapped ions. Further advancement of qubits requires significant contributions from materials science in areas of materials selection, synthesis, fabrication, simulation and characterization. Here, we discuss some of the needs and opportunities for contributions to advance the fundamental understanding of materials used in quantum information applications.

When I began writing this article, it was just the beginning of COVID-19, when we were not yet social distancing. Everything has changed since then, but not a conviction I have disseminated for more than 25 years. More than ever, I maintain that formally addressing the critical visual component of research should be part of every researcher's education. How you visually represent your work not only communicates to others in your discipline. Crafting your visual presentations helps clarify your own thinking and, just as important, is a means of engaging the public. In these challenging times, when society is bombarded with complex information, it is more essential than ever to develop a more accessible and honest visual “language” for the public to understand and gather that information. Formal programs in teaching visual communication will help show the world, outside the research community, how to look at science, understand it, question it, and, hopefully, make smart decisions.

Lithium-ion batteries have enabled the widespread use of portable electronic devices and are propelling the growing electric vehicle market, but new battery technologies with improved performance are necessary for emerging applications such as electric aircraft. The solid-state battery is one such technology that could exhibit enhanced safety and higher energy density compared to conventional lithium-ion batteries. The use of a pure lithium metal anode within solid-state batteries is key for higher energy density (Figure 1a), and it is thought that using solid-state electrolytes instead of conventional liquids could increase the chemical and structural stability of lithium metal.1 Despite continued progress in the development of new inorganic solid-state electrolyte materials; however, a persistent problem has emerged: lithium metal tends to grow as filaments during charging instead of as a flat film, and these filaments can penetrate and fracture the stiff solid-state electrolyte to short circuit the cell (Figure 1b).2–4 To prevent this chemo-mechanical degradation process and enable filament-free charging, it is critical to understand the mechanical properties of lithium metal, which have been elusive because of the highly reactive nature of lithium.

Applying machine learning (ML) methods to accelerate the search for new materials with improved properties has gained increasing attention in recent years. Using nonadaptive ML approaches that do not have an iterative feedback loop can perform poorly in extrapolations at previously unexplored search space, especially when trained on small data sets. We performed numerical simulations on two data sets that exhibit distinct composition–property relationships and explored the relative efficacies of adaptive ML strategies in identifying the optimal material composition with the highest. Adaptive ML methods show promise for extrapolation and find compositions with properties better than those in the training data, but the rate of discovery is dictated by the nuances of the composition–property landscape. The outcome of this work has key implications in developing strategies that employ ML methods for navigating a vast search space of combinatorial possibilities.

Space exemplifies the ultimate test-bed environment for any materials technology. The harsh conditions of space, with extreme temperature changes, lack of gravity and atmosphere, intense solar and cosmic radiation, and mechanical stresses of launch and deployment, represent a multifaceted set of challenges. The materials we engineer must not only meet these challenges, but they need to do so while keeping overall mass to a minimum and guaranteeing performance over long periods of time with no opportunity for repair. Nanophotonic materials—materials that embody structural variations on a scale comparable to the wavelength of light—offer opportunities for addressing some of these difficulties. Here, we examine how advances in nanophotonics and nanofabrication are enabling ultrathin and lightweight structures with unparalleled ability to shape light–matter interactions over a broad electromagnetic spectrum. From solar panels that can be fabricated in space to applications of light for propulsion, the next generation of lightweight and multifunctional photonic materials stands to both impact existing technologies and pave the way for new space technologies.