Explore the vast range of titles available.

Conventional alloys are usually based on a single host metal. Recent high-entropy alloys (HEAs), in contrast, employ multiple principal elements. The strength of HEAs is considerably higher than traditional solid solutions, as the many constituents lead to a rugged energy landscape that increases the resistance to dislocation motion, which can also be retarded by other heterogeneities. The wide variety of nanostructured heterogeneities in HEAs, including those generated on the fly during tensile straining, also offer elevated strain-hardening capability that promotes uniform tensile ductility. Citing recent examples, this review explores the multiple levels of heterogeneities in multi-principal-element alloys that contribute to lattice friction and back stress hardening, as a general strategy towards strength–ductility synergy beyond current benchmark ranges. High entropy alloys are a departure from conventional alloy design. In this review, the authors examine how combinations of hetererogeneities specific to high entropy alloys can overcome the strength–ductility trade-off inherent to traditional alloys.

It has been a long-standing materials science challenge to establish structure-property relations in amorphous solids. Here we introduce a rotationally non-invariant local structure representation that enables different predictions for different loading orientations, which is found essential for high-fidelity prediction of the propensity for stress-driven shear transformations. This novel structure representation, when combined with convolutional neural network (CNN), a powerful deep learning algorithm, leads to unprecedented accuracy for identifying atoms with high propensity for shear transformations (i.e., plastic susceptibility), solely from the static structure in both two- and three-dimensional model glasses. The data-driven models trained on samples at one composition and a given processing history are found transferrable to glass samples with different processing histories or at different compositions in the same alloy system. Our analysis of the new structure representation also provides valuable insight into key atomic packing features that influence the local mechanical response and its anisotropy in glasses. Predicting a priori local defects in amorphous materials remains a grand challenge. Here authors combine a rotationally non-invariant structure representation with deep-learning to predict the propensity for shear transformations of amorphous solids for different loading orientations, only given the static structure.

High-entropy and medium-entropy alloys are presumed to have a configurational entropy as high as that of an ideally mixed solid solution (SS) of multiple elements in near-equal proportions. However, enthalpic interactions inevitably render such chemically disordered SSs rare and metastable, except at very high temperatures. Here we highlight the wide variety of local chemical ordering (LCO) that sets these concentrated SSs apart from traditional solvent-solute ones. Using atomistic simulations, we reveal that the LCO of the multi-principal-element NiCoCr SS changes with alloy processing conditions, producing a wide range of generalized planar fault energies. We show that the LCO heightens the ruggedness of the energy landscape and raises activation barriers governing dislocation activities. This influences the selection of dislocation pathways in slip, faulting, and twinning, and increases the lattice friction to dislocation motion via a nanoscale segment detrapping mechanism. In contrast, severe plastic deformation reduces the LCO towards random SS. Multi-principal-element alloys have been assumed to have the configurational entropy of an ideal solution. Here, the authors use atomistic simulations to show that instead NiCoCr exhibits local chemical order, raising the activation barriers of dislocation activities to elevate mechanical strength.

The global demand for data storage and processing has increased exponentially in recent decades. To respond to this demand, research efforts have been devoted to the development of non-volatile memory and neuro-inspired computing technologies. Chalcogenide phase-change materials (PCMs) are leading candidates for such applications, and they have become technologically mature with recently released competitive products. In this Review, we focus on the mechanisms of the crystallization dynamics of PCMs by discussing structural and kinetic experiments, as well as ab initio atomistic modelling and materials design. Based on the knowledge at the atomistic level, we depict routes to improve the parameters of phase-change devices for universal memory. Moreover, we discuss the role of crystallization in enabling neuro-inspired computing using PCMs. Finally, we present an outlook for future opportunities of PCMs, including all-photonic memories and processors, flexible displays with nanopixel resolution and nanoscale switches and controllers. Chalcogenide phase-change materials (PCMs) are leading candidates for non-volatile memory and neuro-inspired computing devices. This Review focuses on the crystallization mechanisms of PCMs as well as the design principles to achieve PCMs with high switching speeds and good data retention, which will aid the development of PCM-based universal memory and neuro-inspired devices.

Atomistic simulations of dislocation mobility reveal that bodycentered cubic (BCC) high-entropy alloys (HEAs) are distinctly different from traditional BCC metals. HEAs are concentrated solutions in which composition fluctuation is almost inevitable. The resultant inhomogeneities, while locally promoting kink nucleation on screw dislocations, trap them against propagation with an appreciable energy barrier, replacing kink nucleation as the rate-limiting mechanism. Edge dislocations encounter a similar activated process of nanoscale segment detrapping, with comparable activation barrier. As a result, the mobility of edge dislocations, and hence their contribution to strength, becomes comparable to screw dislocations.

“Smaller is stronger.” Nanostructured materials such as thin films, nanowires, nanoparticles, bulk nanocomposites, and atomic sheets can withstand non-hydrostatic (e.g., tensile or shear) stresses up to a significant fraction of their ideal strength without inelastic relaxation by plasticity or fracture. Large elastic strains, up to ∼10%, can be generated by epitaxy or by external loading on small-volume or bulk-scale nanomaterials and can be spatially homogeneous or inhomogeneous. This leads to new possibilities for tuning the physical and chemical properties of a material, such as electronic, optical, magnetic, phononic, and catalytic properties, by varying the six-dimensional elastic strain as continuous variables. By controlling the elastic strain field statically or dynamically, a much larger parameter space opens up for optimizing the functional properties of materials, which gives new meaning to Richard Feynman’s 1959 statement, “there’s plenty of room at the bottom.”

Significance This work demonstrates a structure–property correlation in metallic glasses for the community of amorphous solids. It associates geometrically unfavored motifs, i.e., those most disordered local polyhedral packing structures in a metallic glass, with the soft spots defined from the vibrational modes and correlates them with shear transformation zones composed of atoms with large nonaffine displacements. The statistical correlation established thus ties together the heterogeneity inherent in the amorphous structure with the spatial heterogeneity in the mechanical (elastic and plastic) properties of a metallic glass. In a 3D model mimicking realistic Cu ₆₄Zr ₃₆ metallic glass, we uncovered a direct link between the quasi-localized low-frequency vibrational modes and the local atomic packing structure. We also demonstrate that quasi-localized soft modes correlate strongly with fertile sites for shear transformations: geometrically unfavored motifs constitute the most flexible local environments that encourage soft modes and high propensity for shear transformations, whereas local configurations preferred in this alloy, i.e., the full icosahedra (around Cu) and Z16 Kasper polyhedra (around Zr), contribute the least.

Artificial intelligence and other data-intensive applications have escalated the demand for data storage and processing. New computing devices, such as phase-change random access memory (PCRAM)–based neuro-inspired devices, are promising options for breaking the von Neumann barrier by unifying storage with computing in memory cells. However, current PCRAM devices have considerable noise and drift in electrical resistance that erodes the precision and consistency of these devices. We designed a phase-change heterostructure (PCH) that consists of alternately stacked phase-change and confinement nanolayers to suppress the noise and drift, allowing reliable iterative RESET and cumulative SET operations for high-performance neuro-inspired computing. Our PCH architecture is amenable to industrial production as an intrinsic materials solution, without complex manufacturing procedure or much increased fabrication cost.

Operation speed is a key challenge in phase-change random-access memory (PCRAM) technology, especially for achieving subnanosecond high-speed cache memory. Commercialized PCRAM products are limited by the tens of nanoseconds writing speed, originating from the stochastic crystal nucleation during the crystallization of amorphous germanium antimony telluride (Ge₂Sb₂Te₅). Here, we demonstrate an alloying strategy to speed up the crystallization kinetics. The scandium antimony telluride (Sc0.2Sb₂Te₃) compound that we designed allows a writing speed of only 700 picoseconds without preprogramming in a large conventional PCRAM device. This ultrafast crystallization stems from the reduced stochasticity of nucleation through geometrically matched and robust scandium telluride (ScTe) chemical bonds that stabilize crystal precursors in the amorphous state. Controlling nucleation through alloy design paves the way for the development of cache-type PCRAM technology to boost the working efficiency of computing systems.

High- (and medium-) entropy alloys have emerged as potentially suitable structural materials for nuclear applications, particularly as they appear to show promising irradiation resistance. Recent studies have provided evidence of the presence of local chemical order (LCO) as a salient feature of these complex concentrated solid-solution alloys. However, the influence of such LCO on their irradiation response has remained uncertain thus far. In this work, we combine ion irradiation experiments with large-scale atomistic simulations to reveal that the presence of chemical short-range order, developed as an early stage of LCO, slows down the formation and evolution of point defects in the equiatomic medium-entropy alloy CrCoNi during irradiation. In particular, the irradiation-induced vacancies and interstitials exhibit a smaller difference in their mobility, arising from a stronger effect of LCO in localizing interstitial diffusion. This effect promotes their recombination as the LCO serves to tune the migration energy barriers of these point defects, thereby delaying the initiation of damage. These findings imply that local chemical ordering may provide a variable in the design space to enhance the resistance of multi-principal element alloys to irradiation damage.