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.

The mechanical deformation behavior of nanocomposite metals depends on the dimensions of their constituents, due to the interactions of dislocations with grain and phase boundaries. It is now becoming apparent that the mechanical behavior of these materials also depends on the constituent shapes. This article summarizes experimental and modeling investigations on two types of metal nanocomposites composed of intricately interpenetrating phases—those formed by phase separation in physical vapor codeposited alloys and those synthesized by liquid-metal dealloying. The opportunities and challenges these materials present for investigating complex microstructural morphologies and their effects on mechanical behavior are discussed.

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.

Searching for materials with improved or perhaps completely novel properties involves an iterative process intended to successively narrow the gap between some initial starting point and the desired design target. This can be viewed as an optimization problem in a high-dimensional search space, often with many dozens of material parameters that need to be tuned. To tackle this, the evolutionary process in biology has been a source of inspiration in developing effective search algorithms. However, reaping the full benefits of bioinspired searches for materials design requires some thought. Here, we go beyond traditional black box algorithms and take a broader view of computational evolution strategies. We discuss recent strategies that exploit knowledge about the material configuration statistics and we highlight the advantages when time-varying environments are considered. Throughout, we emphasize that the search strategies themselves can be viewed as a nonequilibrium dynamical process in design space.

With growing demand for better fuel economy for automobiles, multimaterial solutions are increasingly being utilized in the automotive industry for reducing weight in the vehicle body structure. This poses challenges in terms of joining dissimilar metals, especially those with vastly different properties such as aluminum to steel joining. General Motors has developed a new resistance spot-welding technique for dissimilar materials using a multi-ring domed (MRD) electrode and multiple solidification weld schedules to address this challenge. Originally developed for aluminum to aluminum resistance spot welding, this technology is being deployed as the mainstream aluminum joining solution to leverage existing infrastructure and workforce competency in resistance spot welding. With the recent expansion of MRD technology to aluminum to steel resistance spot welding, there is an ever-greater need to experimentally verify the quality of each aluminum to steel resistance spot-weld application with limited time and resources. Nondestructive evaluation (NDE) would enable the transfer of resistance spot-welding technology to dissimilar aluminum to steel joints. This article describes the current state of the art of aluminum to steel resistance spot welding and the challenges in developing a robust NDE process for this technology.

This article focuses on the acoustic-wave enhancement of chemisorption and surface reactions. Acoustic waves generated by a piezoelectric phenomenon on ferroelectric crystals by the application of radio frequency electric power produce periodic lattice distortions at the surface. The effects of surface acoustic waves (SAWs) and the resonance oscillation (RO) of bulk acoustic waves on thin films of metals or metal oxides are described herein. Both SAWs and RO can modify the work functions of thin Ag, Au, or Pd films, and this effect is highly dependent on the surface structures. These changes in the work function can, in turn, affect the adsorptive characteristics of the metals as well as surface reactions and properties such as catalysis. The importance of periodic lattice displacement vertical to the surface is examined in this article, and the acoustic-wave enhancement of metal and metal oxide surfaces as a means of tuning electronic states and chemical properties is discussed.

The recent success of electron cryomicroscopy in biology has drawn the attention of the materials science community, which is starting to employ similar techniques for imaging a wide variety of nonbiological specimens. This article reviews the theory and practical implications of radiation damage in electron microscopy, and then considers how electron cryomicroscopy techniques may be applied to other radiation-sensitive specimens of interest to materials scientists. We also discuss aspects of radiation damage that warrant further study as instrumentation technology advances and consider new methods that might be useful in the future.

At GE Research, we are combining “physics” with artificial intelligence and machine learning to advance manufacturing design, processing, and inspection, turning innovative technologies into real products and solutions across our industrial portfolio. This article provides a snapshot of how this physical plus digital transformation is evolving at GE.

Nature’s optical nanomaterials are poised to form the platform for future optical devices with unprecedented functionality. The brilliant colors of many animals arise from the physical interaction of light with nanostructured, multifunctional materials. While their length scale is typically in the 100-nm range, the morphology of these structures can vary strongly. These biological nanostructures are obtained in a controlled manner, using biomaterials under ambient conditions. The formation processes nature employs use elements of both equilibrium self-assembly and far-from-equilibrium and growth processes. This renders not only the colors themselves, but also the formation processes technologically and ecologically highly relevant. Yet, for many biological nanostructured materials, little is known about the formation mechanisms—partially due to a lack of in vivo imaging methods. Here, we present the toolbox of natural multifunctional nanostructures and the current knowledge about the understanding of their far-from-equilibrium assembly processes.

Phase-change materials (PCMs) have demonstrated a wide range of potential applications ranging from electronic memories to photonic devices. These applications are enabled by the unconventional portfolio of properties that characterizes crystalline PCMs. Here, we address the origin of these unusual properties and how they are related to the application potential of these materials. Evidence will be presented that the properties are related to an unconventional bonding mechanism. Employing a novel map, which separates solids according to the number of electrons transferred and shared between adjacent atoms, it is shown that PCMs occupy a well-defined region. Depicting physical properties such as the optical dielectric constant as the third dimension in the map reveals systematic property trends. Such trends can be utilized to unravel the origins of the unconventional materials properties or alternatively, as a means to optimize them.

High-entropy alloys (HEAs) have been the focus of wide-ranging studies for their applications as next-generation structural materials. For high-temperature industrial applications, creep behavior of structural materials is critical. In addition to high-temperature tensile, compressive, and notched tests, elevated-temperature nanoindentation is a relatively new testing method for HEAs. With the high accuracy of depth-sensing technology and a stable temperature-controlling stage, elevated-temperature time-dependent mechanical behavior of HEAs can be investigated, especially at localized regions without the limitations of the standard specimen size used for traditional creep testing. Also, the creep response from each grain in polycrystalline samples with various crystalline orientations can be explored in detail. This article overviews current progress in studying creep behavior in HEAs via nanoindentation technology.

Superior structural properties of materials are generally desired in harsh environments, such as elevated temperatures, high rates of impact, and radiation. Composite nanolaminates, built with alternating stacks of crystalline layers, each with nanoscale individual thickness, are proving to exhibit many of these target properties. In principle, the nanolaminate concept can be applied to any two-phase, bimetallic system; however, for a number of reasons, they have been limited to combinations of metals with a cubic crystal structure. There is growing demand to increase the number of advanced materials systems containing noncubic metals, since these metals bear several desirable intrinsic properties. In this article, we cover recent modeling and experimental efforts to understand the complexity in structure, mechanisms, and behavior of noncubic/cubic nanolaminates. We hope this article will facilitate and encourage future studies in this promising area.

There has been widespread recent interest in self-assembly and synthesis of porphyrin and its derivatives-based ordered arrays aiming to emulate natural light-harvesting processes and energy storage. However, technologies that leverage the structural advantages of individual porphyrins have not been fully realized and have been limited by available synthesis methods. This article provides general perspectives on porphyrin and derivative chemistry, and discussions on surfactant-assisted cooperative self-assembly using amphiphilic surfactants and functional porphyrins and derivatives. The cooperative self-assembly amplifies the intrinsic advantages of individual porphyrins by engineering them into well-defined one-dimensional–three-dimensional (1D–3D) nanostructures. Surfactant-assisted self-assembly of amphiphilic surfactants and porphyrins has been utilized to form well-defined “micelle-like” nanostructures. Driven by intermolecular interactions, subsequent nucleation and growth confined within these nanostructures lead to the formation of 1D–3D ordered optically and electrically active nanomaterials with structure and function on multiple length scales.

Defects in crystalline solids control the properties of engineered and natural materials, and their characterization focuses our strategies to optimize performance. Electron microscopy has served as the backbone of our understanding of defect structure and their interactions, owing to beneficial spatial resolution and contrast mechanisms that enable direct imaging of defects. These defects reside in complex microstructures and chemical environments, demanding a combination of experimental approaches for full defect characterization. In this article, we describe recent progress and trends in methods for examining defects using scanning electron microscopy platforms. Several emerging approaches offer attractive benefits, for instance, in correlative microscopy across length scales and in in situ studies of defect dynamics.

This article examines two intertwined topics on architected materials with imperfections—their mechanics and optimum design. We first discuss the main factors that control defect sensitivity along with a range of strategies for defect characterization. The potency of both as-designed and as-manufactured defects on their macroscopic response is highlighted with an emphasis on those caused by additive manufacturing technology. As a natural extension of defect sensitivity, we describe the design approaches for architected materials with particular focus on systematic tools of topology optimization. Recent extensions to formally incorporate imperfections in the optimization formulation are discussed, where the ultimate goal is to generate architectures that are flaw-tolerant and perform robustly in the presence of imperfections. We conclude with an outlook on the field, highlighting potential areas of future research.

Modern approaches to alloy design increasingly exploit the framework of computational thermodynamics and kinetics to guide the selection of alloy compositions and processing strategies, to achieve desired microstructures, and yield tailored properties. In this context, phase diagrams play a critical role and their assessment can represent a bottleneck in the design of new multicomponent systems. In recent years, it has become possible to accelerate this process through the coupling of the CALculation of PHAse Diagram (CALPHAD) computational thermodynamics framework with high-throughput quantum mechanical calculations. This article reviews recent developments and applications in this area, and discusses future opportunities for high-throughput calculations in the context of modeling kinetics, highlighting the important role of interfacial processes and atomic mobilities.

Metallic glasses have attractive properties, but since the glassy state is inherently metastable, they are not normally considered for applications at elevated temperatures. Yet, studies have shown that multicomponent and pseudo high-entropy (PHE) compositions can confer useful heat resistance. The formation, thermal stability, and mechanical and chemical properties of multicomponent Fe-(Cr, Mo)- and Zr-based bulk metallic glasses (BMGs) are reviewed to assess their potential as heat-resistant structural materials. The composition Fe43Cr16Mo16C15B10 is castable and fully glassy with rod diameters up to 2.7 mm. Glassy coatings of this material with low porosity, good mechanical properties, and good corrosion resistance can be produced by high-velocity spray coating. The compositions Zr55–65Al7.5–10(TM1,TM2)27.5–35 (TM1 = Fe, Co, Ni, TM2 = Cu, Pd, Ag, Au) yield PHE BMGs, in which a stable cluster-like glassy phase without crystalline precipitates is formed by annealing at temperatures well above the first calorimetric transformation. It is suggested that proliferation of alloy components is an effective method to synthesize metastable metallic materials that retain high strength at elevated temperatures.

Porphyrins are a class of conjugated molecules that structurally and functionally resemble natural photosynthetic and enzymatic chromophores. Crystalline solids self-assembled from anionic and cationic porphyrins yield a new class of multifunctional optoelectronic micro- and nanomaterials. In this article, we provide details on the concept of binary ionic self-assembly (ISA) and ionized forms of porphyrins, as well as formation of hierarchical structures, including nanotubes, rods and ribbons, sheets, and three-dimensional clover-like shapes, spheres, and sheaf-like structures. We summarize key physical properties from ultraviolet–visible characterizations of J-aggregate, exciton delocalization and extended π–π stacking, and related electronic and light-harvesting properties of the structures. Depending on the molecular subunits, the functionalities of the ISA materials are altered. These ISA nanostructures possess attractive light-harvesting and charge- and energy-transport functionalities and allow access to a novel class of nanomaterials with potential for applications in sensors, photovoltaics, photocatalysis, and solar power.

Materials can be endowed with unique properties by the integration of molecular motors. Molecular motors can have a biological origin or can be chemically synthesized and produce work from chemical energy or light. Their ability to access large internal or external reservoirs of energy enables a wide range of nonequilibrium behaviors, including the production of force, changes in shape, internal reorganization, and dynamic changes in mechanical properties—muscle tissue is one illustration of the possibilities. Current research efforts advance our experimental capabilities to create such “active matter” by using either biomolecular or synthetic motors, and also advance our theoretical understanding of these materials systems. Here, we introduce this exciting research field and highlight a few of the recent advances as well as open questions.

Discovering knowledge from data is a quantum jump from quantity to quality, which is the characteristic and the spirit of the development of science. Symbolic regression (SR) is playing a greater role in the discovery of knowledge from data, specifically in this era of exponential data growth, because SRs are able to discover mathematical formulas from data. These formulas may provide scientifically meaningful models, especially when combined with domain knowledge. This article provides an overview of SR applications in the field of materials science and engineering. Integrating domain knowledge with SR is the key and a crucial approach, which allows gaining knowledge from data quickly, accurately, and scientifically. In the data-driven paradigm, SR allows for uncovering the underlying mechanisms of materials behavior, properties, and functions, in a wide range of areas from basic academic research to industrial applications, including experiments and computations, by providing explicit interpretable models from data, in comparison with other machine-learning “black-box” models. SR will be a powerful tool for rational and automatic materials development.