This work aims to predict the microstructure of recrystallized medium and high-entropy alloys (MEAs and HEAs) with a face-centered cubic structure, in particular the density of annealing twins and their thickness. Eight MEAs and five HEAs from the Cr-Mn-Fe-Co-Ni system are considered, which have been cast, homogenized, cold-worked and recrystallized to obtain different grain sizes. This work thus provides a database that could be used for data mining to take twin boundary engineering for alloy development to the next level. Since the stacking fault energy is known to strongly affect recrystallized microstructures, the latter was determined at 293 K using the weak beam dark-field technique and compared with ab initio simulations, which additionally allowed to calculate its temperature dependence. Finally, we show that all these data can be rationalized based on theories and empirical relationships that were proposed for pure metals and binary Cu-based alloys.

In multicomponent materials, short-range order (SRO) is the development of correlated arrangements of atoms at the nanometer scale. Its impact in compositionally complex materials has stimulated an intense debate within the materials science community. Understanding SRO is critical to control the properties of technologically relevant materials, from metallic alloys to functional ceramics. In contrast to long-range order, quantitative characterization of the nature and spatial extent of SRO evades most of the experimentally available techniques. Simulations at the atomistic scale have full access to SRO but face the challenge of accurately sampling high-dimensional configuration spaces to identify the thermodynamic and kinetic conditions at which SRO is formed and what impact it has on material properties. Here we highlight recent progress in computational approaches, such as machine learning-based interatomic potentials, for quantifying and understanding SRO in compositionally complex materials. We briefly recap the key theoretical concepts and methods.

Surface compositions play a predominant role in the efficiency and lifetime of membranes and catalysts. The surface composition can change during operation due to segregation, thus controlling and predicting the surface composition is essential. Computational modelling can aid in predicting alloy stability, along with designing surface alloys and near-surface alloys that can outperform existing materials. In this work, a computational model to predict surface segregation in ternary alloys is developed. The model, based on Miedema's semi-empirical model and Monte Carlo simulations, enables to predict long- and short-range ordering in the surface and subsurface layers. It is used to screen a vast range of alloy compositions to design a novel ternary Pd-based material for H₂ separation membranes. The addition of specific amounts of Cu and Zr to Pd is expected to reduce poisoning and enhance the permeability as compared to pure Pd.

High-entropy alloys are solid solutions of multiple principal elements that are capable of reaching composition and property regimes inaccessible for dilute materials. Discovering those with valuable properties, however, too often relies on serendipity, because thermodynamic alloy design rules alone often fail in high-dimensional composition spaces. We propose an active learning strategy to accelerate the design of high-entropy Invar alloys in a practically infinite compositional space based on very sparse data. Our approach works as a closed-loop, integrating machine learning with density-functional theory, thermodynamic calculations, and experiments. After processing and characterizing 17 new alloys out of millions of possible compositions, we identified two high-entropy Invar alloys with extremely low thermal expansion coefficients around 2 × 10-6 per degree kelvin at 300 kelvin. We believe this to be a suitable pathway for the fast and automated discovery of high-entropy alloys with optimal thermal, magnetic, and electrical properties.

Physical properties of ten single-phase FCC Cr_xMn₂₀Fe₂₀Co₂₀Ni_40-x high-entropy alloys (HEAs) were investigated for 0 ≤ x ≤ 26 at%. The lattice parameters of these alloys were nearly independent of composition while solidus temperatures increased linearly by ∼30 K as x increased from 0 to 26 at.%. For x ≥ 10 at.%, the alloys are not ferromagnetic between 100 and 673 K and the temperature dependencies of their coefficients of thermal expansion and elastic moduli are independent of composition. Magnetic transitions and associated magnetostriction were detected below ∼200 K and ∼440 K in Cr₅Mn₂₀Fe₂₀Co₂₀Ni₃₅ and Mn₂₀Fe₂₀Co₂₀Ni₄₀, respectively. These composition and temperature dependencies could be qualitatively reproduced by ab initio simulations that took into account a ferrimagnetic ↔ paramagnetic transition. Transmission electron microscopy revealed that plastic deformation occurs initially by the glide of perfect dislocations dissociated into Shockley partials on {111} planes. From their separations, the stacking fault energy (SFE) was determined, which decreases linearly from 69 to 23 mJ·m⁻² as x increases from 14 to 26 at.%. Ab initio simulations were performed to calculate stable and unstable SFEs and estimate the partial separation distances using the Peierls-Nabarro model. While the compositional trends were reasonably well reproduced, the calculated intrinsic SFEs were systematically lower than the experimental ones. Our ab initio simulations show that, individually, atomic relaxations, finite temperatures, and magnetism strongly increase the intrinsic SFE. If these factors can be simultaneously included in future computations, calculated SFEs will likely better match experimentally determined SFEs.

Precipitation strengthening has been the basis of physical metallurgy since more than 100 years owing to its excellent strengthening effects. This approach generally employs coherent and nano-sized precipitates, as incoherent precipitates energetically become coarse due to their incompatibility with matrix and provide a negligible strengthening effect or even cause brittleness. Here we propose a shear band-driven dispersion of nano-sized and semicoherent precipitates, which show significant strengthening effects. We add aluminum to a model CoNiV medium-entropy alloy with a face-centered cubic structure to form the L2₁ Heusler phase with an ordered body-centered cubic structure, as predicted by ab initio calculations. Micro-shear bands act as heterogeneous nucleation sites and generate finely dispersed intragranular precipitates with a semicoherent interface, which leads to a remarkable strength-ductility balance. This work suggests that the structurally dissimilar precipitates, which are generally avoided in conventional alloys, can be a useful design concept in developing high-strength ductile structural materials.

Besides revealing excellent mechanical properties, compositionally complex alloys are also very promising candidates for applications in heterogeneous catalysis. The opportunity provided by the tremendously large composition phase space to explore new materials and tune the materials properties in the materials design cycle is, however, intrinsically coupled with the challenge of controlling surface segregation, which is generally more severe in multicomponent alloys as compared to simpler systems. We demonstrate this by computing the surface phase diagram of two candidate compositionally complex catalysts. Significant surface segregation is found even at very high temperature and this can strongly affect the catalytic properties of these alloys. We explain the observed phase stabilities in terms of segregation energies and rationalize the segregation trends with canonical models. Finally, we propose a set of descriptors accessible with first-principles calculations that allows to quickly incorporate the evaluation of the segregation during the alloy design process.

We use an analytical model to propose candidate compositionally complex alloys of the Mo-Nb-Ta-W family with optimal yield stress. We then introduce a computationally tractable method based on first-principles calculations to model phase equilibria in complex alloys at arbitrary concentrations. We utilize this method to predict the phase diagram at the optimized compositions and observe a tendency towards ordering for some of the proposed alloys. By combining yield stress data and thermodynamic equilibria, we suggest two alloy compositions with optimal mechanical properties and a strong solid solution forming ability for further experimental validation.

The field of atomistic simulations of multicomponent materials and high entropy alloys is progressing rapidly, with challenging problems stimulating new creative solutions. In this Perspective, we present three topics that emerged very recently and that we anticipate will determine the future direction of research of high entropy alloys: the usage of machine-learning potentials for very accurate thermodynamics, the exploration of short-range order and its impact on macroscopic properties, and the more extensive exploitation of interstitial alloying and high entropy alloy surfaces for new technological applications. For each of these topics, we briefly summarize the key achievements, point out the aspects that still need to be addressed, and discuss possible future improvements and promising directions.

The reactivity of the surface of multicomponent metals such as High Entropy Alloys (HEAs) is rapidly gaining importance for corrosion and catalytic applications, but the mechanisms of surface segregation in these complex materials are poorly understood. Here we investigate with ab initio calculations the segregation thermodynamics in the magnetic Cr-Mn-Fe-Co-Ni HEA in vacuum, in the presence of O at the surface, and in the oxide phase. We predict a weak segregation of Ni for Cr-, Mn-, and Fe-rich alloys in vacuum and a very strong segregation of Cr and Mn upon exposure to O.