Search

Metallurgists have long been accustomed to a trade-off between yield strength and tensile ductility. Extending previously known strain-hardening mechanisms, the emerging multi-principal-element alloys (MPEAs) offer additional help in promoting the strength-ductility synergy, towards gigapascal yield strength simultaneously with pure-metal-like tensile ductility. The highly concentrated chemical make-up in these 'high-entropy' alloys (HEAs) adds, at ultrafine spatial scale from sub-nanometer to tens of nanometers, inherent chemical inhomogeneities in local composition and local chemical order (LCO). These institute a 'nano-cocktail' environment that exerts extra dragging forces, rendering a much wavier motion of dislocation lines (in stick-slip mode) different from dilute solutions. The variable fault energy landscape also makes the dislocation movement sluggish, increasing their chances to hit one another and react to increase entanglement. The accumulation of dislocations (plus faults) dynamically stores obstacles against ensuing dislocation motion to sustain an adequate strain-hardening rate at high flow stresses, delaying plastic instability to enable large (uniform) elongation. The successes summarized advocate MPEAs as an effective recipe towards ultrahigh strength at little expense of tensile ductility. The insight gained also answers the question as to what new mechanical behavior the HEAs have to offer, beyond what has been well documented for traditional metals and solid solutions.

Plastic flow in amorphous solids is known to be carried by localized shear transformations (STs) which have been proposed to preferentially initiate from some defect units in the structure, akin to dislocations and point defects in crystalline solids. Despite the central role of STs in the mechanical deformation of metallic glasses (MGs), our knowledge of their characteristics has so far been limited to hypothetical models, based on computer simulations using unreleastically high cooling rates. Using combined molecular dynamics (MD) and Monte Carlo (MC) simulations, here we have succeeded in solidifying a metallic liquid at an effective cooling rate as slow as 500 K/s to approach that typical in experiments for producing bulk MGs. Exploiting this realistic MG model, we find that STs do not arise from signature structural defects that can be recognized a priori. Upon yielding, only about 2% of the total atoms participate in STs, each event involving as few as ~10 atoms. These findings rectify the unrealistically high content of liquid-like regions retained in MD-produced glass structures, which has rendered the MG model artificially ductile and under-predicted the sample-wide shear modulus by at least ~20% (with respect to that of experimental BMGs). Our finding sheds light on the scope of intrinsic structural inhomogeneity as well as the indeterministic aspect of the ST emergence under mechanical loading., Comment: 29 pages, 11 figures, 1 table

It has been a long-standing materials science challenge to establish structure-property relations in amorphous solids. Here we introduce a rotation-variant 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 - the spatial atomic positions - 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.

The elementary excitations in metallic glasses (MGs), i.e., $\beta$ processes that involve hopping between nearby sub-basins, underlie many unusual properties of the amorphous alloys. A high-efficacy prediction of the propensity for those activated processes from solely the atomic positions, however, has remained a daunting challenge. Recently, employing well-designed site environment descriptors and machine learning (ML), notable progress has been made in predicting the propensity for stress-activated $\beta$ processes (i.e., shear transformations) from the static structure. However, the complex tensorial stress field and direction-dependent activation would induce non-trivial noises in the data, limiting the accuracy of the structure-property mapping learned. Here, we focus on the thermally activated elementary excitations and generate high-quality data in several Cu-Zr MGs, allowing quantitative mapping of the potential energy landscape. After fingerprinting the atomic environment with short- and medium-range interstice distribution, ML can identify the atoms with strong resistance or high compliance to thermal activation, at an unprecedented accuracy over ML models for stress-driven activation events. Interestingly, a quantitative "between-task" transferring test reveals that our learnt model can also generalize to predict the propensity of shear transformation. Our dataset is potentially useful for benchmarking future ML models on structure-property relationships in MGs.

High-entropy alloys (HEAs) were 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 a structural feature that sets these concentrated SSs apart from traditional solvent-solute ones: the HEAs possess a wide variety of (local) chemical ordering (LCO). Our atomistic simulations employing an empirical interatomic potential for NiCoCr reveal that the LCO of the multi-principal-element SS changes conspicuously with alloy processing conditions, producing a wide range of generalized planar fault energy in terms of both its sample-average and spatial variation. We further demonstrate that the LCO heightens the ruggedness of the energy landscape and raises activation barriers governing dislocation activities. This not only influences the selection of dislocation pathways in slip, faulting, twinning, and martensitic transformation, but also increases the lattice friction to dislocation motion via a new mechanism of nanoscale segment detrapping that elevates the mechanical strength. All these open a vast playground not accessible to ground-state SSs or intermetallics, offering rich opportunities to tune properties.

Fouling of surfaces leads to performance degradation in many energy-intensive industrial processes, but the present solutions are either too complicated to be routinely used or incomplete for eradication. Here we propose and demonstrate that carbon-containing deposits can be catalytically wiped out in an efficient way by roaming palladium nanoparticles with extreme shape flexibility at relatively low temperatures. Surprisingly, during their dramatic liquid-like migrations, these particles could still maintain crystalline interior and conserve their initial crystal orientations through self-surface diffusion. Moreover, these catalytic particles were even able to become regenerated by other roaming particles after occasionally deactivated by surface coking or multiple-particle sintering. These findings shed light on metabolically driven, "living" nanocrystals, and also open a new avenue for efficient catalysis.

Using molecular dynamics simulations, we have studied the atomic correlations characterizing the second peak in the radial distribution function (RDF) of metallic glasses and liquids. The analysis was conducted from the perspective of different connection schemes of atomic packing motifs, based on the number of shared atoms between two linked coordination polyhedra. The results demonstrate that the cluster connections by face-sharing, specifically with three common atoms, are most favored when transitioning from the liquid to glassy state, and exhibit the stiffest elastic response during shear deformation. These properties of the connections and the resultant atomic correlations are generally the same for different types of packing motifs in different alloys. Splitting of the second RDF peak was observed for the inherent structure of the equilibrium liquid, originating solely from cluster connections; this trait can then be inherited in the metallic glass formed via subsequent quenching of the parent liquid through the glass transition, in the absence of any additional type of local structural order. Increasing ordering and cluster connection during cooling, however, may tune the position and intensity of the split peaks., Comment: 25 pages, 5 figures