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.

Robots and artificial machines have been captivating the public for centuries, depicted first as threats to humanity, then as subordinates and helpers. In the last decade, the booming exposure of humans to robots has fostered an increasing interest in soft robotics. By empowering robots with new physical properties, autonomous actuation, and sensing mechanisms, soft robots are making increasing impacts on areas such as health and medicine. At the same time, the public sympathy to robots is increasing. However, there is still a great need for innovation to push robotics toward more diverse applications. To overcome the major limitation of soft robots, which lies in their softness, strategies are being explored to combine the capabilities of soft robots with the performance of hard metallic ones by using composite materials in their structures. After reviewing the major specificities of hard and soft robots, paths to improve actuation speed, stress generation, self-sensing, and actuation will be proposed. Innovations in controlling systems, modeling, and simulation that will be required to use composite materials in robotics will be discussed. Finally, based on recently developed examples, the elements needed to progress toward a new form of artificial life will be described.

This article reviews the concept of metastability in alloy design. While most materials are thermodynamically metastable at some stage during synthesis and service, we discuss here cases where metastable phases are not coincidentally inherited from processing, but rather are engineered. Specifically, we aim at compositional (partitioning), thermal (kinetics), and microstructure (size effects and confinement) tuning of metastable phases so that they can trigger athermal transformation effects when mechanically, thermally, or electromagnetically loaded. Such a concept works both at the bulk scale and also at a spatially confined microstructure scale, such as at lattice defects. In the latter case, local stability tuning works primarily through elemental partitioning to dislocation cores, stacking faults, interfaces, and precipitates. Depending on stability, spatial confinement, misfit, and dispersion, both bulk and local load-driven athermal transformations can equip alloys with substantial gain in strength, ductility, and damage tolerance. Examples include self-organized metastable nanolaminates, austenite reversion steels, metastable medium- and high-entropy alloys, as well as steels and titanium alloys with martensitic phase transformation and twinning-induced plasticity effects.

Predicting the structural response of advanced multiphase alloys and understanding the underlying microscopic mechanisms that are responsible for it are two critically important roles that modeling plays in alloy development. The demonstration of superior properties of an alloy, such as high strength, creep resistance, high ductility, and fracture toughness, is not sufficient to secure its use in widespread applications. Still, a good model is needed to take measurable alloy properties, such as microstructure and chemical composition, and forecast how the alloy will perform in specified mechanical deformation conditions, including temperature, time, and rate. Here, we highlight recent achievements using multiscale modeling in elucidating the coupled effects of alloying, microstructure, and mechanism dynamics on the mechanical properties of polycrystalline alloys. Much of the understanding gained by these efforts relies on the integration of computational tools that vary over many length scales and time scales, from first-principles density functional theory, atomistic simulation methods, dislocation and defect theory, micromechanics, phase-field modeling, single crystal plasticity, and polycrystalline plasticity.

Historically, the advent of robotics has important roots in metallurgy. The first industrial robot, Unimate, was used by General Motors to handle hot metal—transporting die castings and welding them to an automotive body. Now, nearly 60 years later, metallurgical use of robotics is still largely confined to automation of dangerous, complex, and repetitive tasks. Beyond metallurgy, the field of autonomy is undergoing a renaissance, impacting applications from pharmaceuticals to transportation. In this article, we review the emerging elements of high-throughput experimental automation, which, when combined with artificial intelligence or machine-learning systems, will enable autonomous discovery of novel alloys and process routes.

Over the past three years, my colleagues and I have embarked on an exciting journey into electric-field control of magnetism, parts of which we describe in this article. What we present to you is something that we believe is extremely exciting from both a fundamental science and applications perspective, and has the potential to revolutionize our world. Needless to say, this will require a lot of new innovations, both in the fundamental science arena as well as translating scientific discoveries into real applications. We hope this article will help spur more research in electric-field control of magnetism within the broad materials community.

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.