Cryogenic transmission electron microscopy is simply transmission electron microscopy conducted on specimens that are cooled in the microscope. The target temperature of the specimen might range from just below ambient temperature to less than 4 K. In general, as the temperature decreases, cost increases, especially below –77°C when liquid He is required. We have two reasons for wanting to cool the specimen—improving stability of the material or observing a material whose properties change at lower temperatures. Both types of study have a long history. The cause of excitement in this field today is that we have a perfect storm of research activity—electron microscopes are almost stable with minimal drift (we can correct what drift there is), we can prepare specimens from the bulk or build them up, we have spherical-aberration-corrected lenses and monochromated beams, we have direct-electron-detector cameras, and computers are becoming powerful enough to handle all the data we produce.

Machine learning (ML) and artificial intelligence (AI) are quickly becoming commonplace in materials research. In addition to the standard workflow of fitting a model to a large set of data in order to make predictions, the materials community is finding novel and meaningful ways to integrate AI within their work. This has led to an acceleration not only of materials design and discovery, but also of other aspects of materials research as well, including faster computational models, the development of autonomous and intelligent “robot researchers,” and the automatic discovery of physical models. In this issue, we highlight a few of these applications and argue that AI/ML is delivering real-world, practical solutions to materials problems. It is also clear that we need AI/ML methods and models, “dialects” that are better adapted to materials research.

In situ nanomechanical testing provides critical insight into the fundamental processes that lead to deformation phenomena in materials. Often, in situ tests are performed in relevant conditions such as high or low temperatures, tribological contact, gas environments, or under radiation exposure. Modern diffraction and imaging methods of materials under load provide high spatial resolution and enable extraction of quantitative mechanical data from local microstructure components or nano-sized objects. The articles in this issue cover recent advances in different types of in situ nanomechanical testing methods, spanning from dedicated nanomechanical testing platforms and microelectromechanical systems devices to deformation analyses via in situ diffraction and imaging methods. This includes scanning electron microscopy, advanced scanning transmission electron microscopy, electron diffraction, x-ray diffraction, and synchrotron techniques. Emerging areas such as in situ tribology enable novel insights into the origin of deformation mechanisms, while the evolution of microelectromechanical systems for controlled in situ testing provide opportunities for advanced control and loading strategies. Discussion on the current state of the art for in situ nanomechanical testing and future opportunities in imaging, strain sensing, and testing environments are also addressed.

The discovery, design, and development of new alloys have long been critical elements of advanced engineering systems. Challenged by their chemical and structural complexity, this design process is, however, often too slow. This article highlights progress in theory, computation, data, and advanced experimental techniques that are advancing our capabilities for rapid discovery and design of new multicomponent alloys. Applied across the length scales, these new capabilities support exploration across broad composition spaces; examples of new materials and associated advances in the understanding of underlying thermochemical and thermomechanical phenomena are presented. We highlight current challenges, gaps, and specific areas that, if further developed, could have future high payoff.

Porphyrin, as a planar macrocyclic molecule, extensively exists naturally in plants and animals and plays an important role in life activities. Normally, porphyrin exists in the form of nanostructures/aggregations through molecular self-assembly. Thus, it is of great interest for tuning nanostructures, understanding mechanisms, and exploring the diverse applications. In this issue, we present articles covering the synthesis and formation mechanisms of porphyrin nanostructures by self-assembly methods and their applications in solar-energy harvesting, water splitting, environmental pollutant reduction, and nanomedicine for tumor therapy. These articles present the recent developments and potential research directions of this field, and we hope they will interest and inspire readers to enter this growing field.

While initial interest in nanocomposites was to explore size effects in mechanical behavior, current research is focused around the new paradigm of interface-dominated/enabled mechanical behavior. The use of advanced computational tools to accelerate the discovery, design, and fundamental understanding of behavior is highlighted along with novel synthesis and in situ nanomechanical characterization tools. Designed interfaces can be used as building blocks to create new forms of hierarchical composites with unprecedented mechanical and physical behavior. New frontiers in the field of mechanical behavior of nanocomposites involving metallic, nanocarbon, bioinspired, and biomaterials are reviewed.

The rapidly growing demand for data storage and processing, driven by artificial intelligence (AI) and other data-intensive applications, is posing a serious challenge for current computing devices based on the von Neumann architecture. For every calculation, data sets need to be shuffled sequentially between the processor, and multiple memory and storage units through bandwidth-limited and energy-inefficient interconnects, typically causing 40% power wastage. Phase-change materials (PCMs) show great promise to break this bottleneck by enabling nonvolatile memory devices that can optimize the complex memory hierarchy, and neuro-inspired computing devices that can unify computing with storage in memory cells. The articles in this issue of MRS Bulletin highlight recent breakthroughs in the fundamental materials science, as well as electronic and photonic implementations of these novel devices based on PCMs.

Tougher, lighter, and more formable and machinable metals for broader ranges of applications at higher temperatures are needed now more than ever. High-performance computing, high-resolution microscopy, and advanced spectroscopy methods, including neutrons and synchrotron x-rays, together with advances in metallurgy and metal mixology, reveal the potential of multicomponent advanced metals, such as multicomponent bulk metallic glasses and advanced high-entropy alloys. The development of new experimental approaches relates bulk properties and voxel-associated optimized properties throughout structures with high resolution. The correlations from in situ measurements greatly improve crystal plasticity-based models. This issue of MRS Bulletin overviews recent progress in the field, and this article highlights the importance of these new perspectives. The latest progress and directions in the science and technology for prospective high-temperature metals for structural applications are reported.

The transportation sector is the largest contributor to greenhouse gas emissions in the United States. One method being used to reduce greenhouse emissions related to the transportation sector is improving vehicle fuel efficiency through mass reduction. Reducing the mass of on-highway passenger vehicles by 10% can result in vehicle fuel economy improvements of as much as 6–8% if the powertrain is downsized to maintain equivalent performance. Some of the materials being investigated and implemented to reduce passenger vehicle mass include advanced high-strength steel, aluminum, magnesium, and polymer composites. Additionally, multimaterial structures that allow for optimal combinations of lightweight materials to achieve maximum weight reduction with lowest cost and best structural performance have recently become of particular interest. However, assembling multimaterial structures can be challenging due to differences in melting temperature and coefficient of thermal expansion of different materials, as well as formation of intermetallic compounds and galvanic corrosion potential. Joining technologies for lightweight multimaterial structures must address these challenges to be successful. This article highlights advances made in five different joining techniques: nondestructive evaluation of resistance spot-welded aluminum to steel, modeling of structural adhesives, temperature control of friction stir welds, ultrasonic welding of magnesium, and vapor foil actuation welding.

The integration of materials and architectural features at multiple length scales into structural mechanics has shifted the paradigm of structural design toward optimally engineered structures, which resulted in, for example, the Eiffel Tower. This structural revolution paved the way for the development of computational design approaches used in modern-day construction. Similar principles are now being applied to the design and manufacture of architected materials with a suite of properties determined a priori and attained through multiscale approaches. These new material classes potentially offer breakthrough advances in almost every branch of technology: from ultra-lightweight and damage-tolerant structural materials to safe and efficient energy storage, biomedical devices, biochemical, and micromechanical sensors and actuators, nanophotonic devices, and textiles. When reduced to the microscale, such materials embody the characteristics of both the constituent material, which brings the effects of its microstructure and ensuing properties at the relevant characteristic length scales, as well as the structure, which is driven by architected design. This issue gives an overview of the current state of the art of this new class of materials.

Traditional approaches to materials synthesis have largely relied on uniform, equilibrated phases leading to static “condensed-matter” structures (e.g., monolithic single crystals). Departures from these modes of materials design are pervasive in biology. From the folding of proteins to the reorganization of self-regulating cytoskeletal networks, biological materials reflect a major shift in emphasis from equilibrium thermodynamic regimes to out-of-equilibrium regimes. Here, equilibrium structures, determined by global free-energy minima, are replaced by highly structured dynamical states that are out of equilibrium, calling into question the utility of global thermodynamic energy minimization as a first-principles approach. Thus, the creation of new materials capable of performing life-like functions such as complex and cooperative processes, self-replication, and self-repair, will ultimately rely upon incorporating biological principles of spatiotemporal modes of self-assembly. Elucidating fundamental principles for the design of such out-of-equilibrium dynamic self-assembling materials systems is the focus of this issue of MRS Bulletin.

The coupling of acoustic energy with materials structures and processes is at the core of such current and emerging application areas as ultrasound-enabled materials characterization, structuring, and processing. High concentration of acoustic energy, such as upon the collapse of a cavitation bubble, has been shown to provide conditions for the synthesis of unusual material phases and structures, while intriguing reports on acoustic activation of surface diffusion, desorption, and catalysis hold high promise for applications where heating must be avoided or rapid switching of surface conditions is required. Some of the recent scientific and technical advances in the general area of acoustically enabled materials synthesis, processing, and characterization are reviewed in this issue of MRS Bulletin. Additional discussion of experimental data and computational results providing insights into the fundamental mechanisms and channels of the acoustic energy coupling to atomic-scale surface features and adsorbates is also provided in this article.

Alloy design is critical to achieving the target performance of industrial components and products. In designing new alloys, there are multiple property requirements, including mechanical, environmental, and physical properties, as well as manufacturability and processability. Computational models and tools to predict properties from alloy compositions and to optimize compositions for multiple objectives are essential in enabling efficient, robust alloy design. Data-driven property models by machine learning (ML) are particularly useful in predicting physical properties with relatively simple dependence on composition, and in predicting complex properties that are too difficult for a physics-based model to achieve with desirable accuracy. In this article, we describe examples of ML applications to model coefficient of thermal expansion, creep and fatigue resistance in designing Ni-based superalloys, and optimization methodologies. We also discuss physics-based microstructure models that have been developed for optimizing heat-treatment conditions to achieve desired microstructures.

Conventional alloy design based on a single primary element has reached its limits in terms of performance optimization. An alloy design strategy with multi-principal elements has recently been uncovered to overcome this bottleneck. Multicomponent alloys, generally referred to as high-entropy alloys (HEAs), exhibit many promising properties, especially outstanding mechanical performance at cryogenic, ambient, and elevated temperatures. In this article, we focus on precipitation-hardened HEAs, which are potential candidates for next-generation structural materials, especially at high temperatures. The key issues involved include precipitation behaviors, phase stability, and phase control, all of which provide useful guidelines for further development of high-temperature materials with superior performance. In particular, we address the formation of cellular γ′ precipitates at grain boundaries, which is closely related to the embrittlement of HEAs at intermediate temperatures. Critical issues and design strategies in developing HEAs for high-temperature applications are also discussed.

Linear elastic moduli of solids with similar chemical compositions usually vary fairly insignificantly. However, for a broad class of apparently similar materials, their higher-order (nonlinear) moduli may differ by many times or even by orders of magnitude. Besides their large magnitude, nonlinear effects often demonstrate qualitative/functional features inconsistent with predictions of the classical theory of nonlinear elasticity based on consideration of weak lattice (atomic) nonlinearity. The latter is mostly applicable to ideal crystals and flawless amorphous solids, whereas the presence of structural heterogeneities can drastically modify the acoustic nonlinearity of materials without appreciable variation in the linear elastic properties. Despite often rather nontrivial/nonstraightforward relationships between microstructural features of the material and the resultant “nonclassical” acoustic nonlinearity, the extremely high structural sensitivity makes utilization of nonlinear acoustic effects attractive for a broad range of diagnostic applications that have been emerging in recent years in various areas—from seismic sounding and nondestructive testing to materials characterization down to the nanoscale.

The design and engineering of the size and shapes of photoactive building blocks enable the fabrication of functional nanocrystals, especially for applications in light harvesting, photocatalytic synthesis, water splitting, and photodegradation. Synthesis of such nanocrystals has been demonstrated recently through noncovalent interactions such as π–π stacking and ligand coordination using optically active porphyrin as a functional building block. Depending on the kinetic conditions, the resulting nanocrystals exhibit well-defined one- to three-dimensional shapes such as spheres, nanowires, and nano-octahedra. These well-defined porphyrin nanocrystals show interesting size- and shape-dependent photocatalytic activity. This article reviews the synthesis and formation of porphyrin nanocrystals with controlled size and shape. Important photocatalytic processes such as photodegradation of organic pollutants, photocatalytic water splitting and hydrogen production, and photosynthesis of metallic fuel-cell catalysts are highlighted. Insights on size- and shape-dependent properties are discussed.

A material’s properties are derived from its constituent material composition and its structural hierarchy across length scales down to the nanometer level. At submicron length scales, materials exhibit unique size-affected mechanical properties such as enhanced strength, ductility, and flaw tolerance, but these are generally lost in bulk materials. Emerging fabrication methods have enabled the creation of materials with controllable architectures down to the nanoscale. These micro- and nanoarchitected materials utilize both resilient architectures and size-affected constituent materials to achieve unprecedented mechanical properties such as ultrahigh strength at low density, recoverability after large applied strains in intrinsically brittle materials, and metamaterial properties such as chirality and negative static compressibility. In this article, we describe the governing principles behind these materials and outline recent progress in the field. We unravel the details of the deformation and failure processes to facilitate a fundamental understanding of effective materials properties and provide a guideline for the design of the next generation of nanoarchitected materials.

Continued progress in artificial intelligence (AI) and associated demonstrations of superhuman performance have raised the expectation that AI can revolutionize scientific discovery in general and materials science specifically. We illustrate the success of machine learning (ML) algorithms in tasks ranging from machine vision to game playing and describe how existing algorithms can also be impactful in materials science, while noting key limitations for accelerating materials discovery. Issues of data scarcity and the combinatorial nature of materials spaces, which limit application of ML techniques in materials science, can be overcome by exploiting the rich scientific knowledge from physics and chemistry using additional AI techniques such as reasoning, planning, and knowledge representation. The integration of these techniques in materials-intelligent systems will enable AI governance of the scientific method and autonomous scientific discovery.

Lightweighting of vehicles and portable structures is an important undertaking. Multimaterial design is required to achieve conflicting design targets such as cost, stiffness, and weight. Friction stir welding (FSW) variants, such as friction stir dovetailing and friction stir scribe, are enabling technologies for joining of dissimilar metals. This article discusses how FSW variants are capable of joining aluminum to steel in particular. The characteristics of metallurgical bonding at the dissimilar materials interface are strongly affected by weld temperature. Control of FSW process temperature enables metallurgical bonding with suppressed formation of intermetallics at the dissimilar materials interface, resulting in improved mechanical properties relative to competing techniques. Temperature control is thus a powerful tool for process development and ensuring weld quality of dissimilar materials welds.

While fast-switching rewritable nonvolatile memory units based on phase-change materials (PCMs) are already in production at major technology companies such as Intel (16–64 GB chips are currently available), an in-depth understanding of the physical factors that determine their success is still lacking. Recently, we have argued for a liquid-phase metal-to-semiconductor transition (M-SC), located not far below the melting point, Tm, as essential. The M-SC is itself a consequence of atomic rearrangements that are involved in a fragile-to-strong viscosity transition that controls both the speed of crystallization and the stabilization of the semiconducting state. Here, we review past work and introduce a new parameter, the “metallicity” (inverse of the average Pauling electronegativity of a multicomponent alloy). When Tm-scaled temperatures of known M-SCs of Group IV, V, and VI alloys are plotted against their metallicities, the curvilinear plot leads directly to the composition zone of all known PCMs and the temperature interval below Tm, where the transition should occur. The metallicity concept could provide guidance for tailoring PCMs.