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Abstract Soft magnetic materials (SMMs) are indispensable for electromechanical energy conversion in high-efficiency applications, but they are exposed to increasing mechanical loading conditions in electric motors due to higher rotational speeds. Enhancing the yield strength of SMMs is essential to prevent the degradation in magnetic performance and failure from plastic deformation, yet most SMMs have yield strengths far below one gigapascal. Here, we present a multicomponent nanostructuring strategy that doubles the yield strength of SMMs while maintaining ductility. We introduce morphologically anisotropic nanoprecipitates through dislocation-driven precipitation induced by preceding deformation during heat treatment in an iron–nickel–cobalt–tantalum material. With all dimensions of the precipitates below the magnetic domain wall width, we achieve a high precipitate number density with a large specific surface area, small interprecipitate spacing, and high lattice mismatch, which impede dislocation glide and strengthen the material. Both the matrix and precipitates are ferromagnetic, yielding a high magnetic moment. This nanostructuring approach offers a pathway to two-gigapascal-strong ductile SMMs with moderately increased coercivity that can be tolerated in exchange for significantly improved mechanical performance for sustainable electrification.

Abstract Solid-state Li-ion batteries, based on Ni-rich oxide cathodes and Li-metal anodes, can theoretically reach a high specific energy of 393 Wh kg−1 and hold promise for electrochemical storage. However, Li intercalation-induced dimensional changes can lead to crystal defect formation in these cathodes, and contact mechanics problems between cathode and solid electrolyte. Understanding the interplay between cathode microstructure, operating conditions, micromechanics of battery materials, and capacity decay remains a challenge. Here, we present a microstructure-sensitive chemo-mechanical model to study the impact of grain-level chemo-mechanics on the degradation of composite cathodes. We reveal that crystalline anisotropy, state-of-charge-dependent Li diffusion rates, and lattice dimension changes drive dislocation formation in cathodes and contact loss at the cathode/electrolyte interface. These dislocations induce large lattice strain and trigger oxygen loss and structural degradation preferentially near the surface area of cathode particles. Moreover, contact loss is caused by the micromechanics resulting from the crystalline anisotropy of cathodes and the mechanical properties of solid electrolytes, not just operating conditions. These findings highlight the significance of grain-level cathode microstructures in causing cracking, formation of crystal defects, and chemo-mechanical degradation of solid-state batteries.

Abstract Materials with well-defined electrical resistivity that does not change with temperature or time are important in robotics, communication and automation. However, the challenge of designing such materials has remained elusive due to the temperature-dependent electron-phonon scattering. Moreover, resistive electrical conductors used in flexible and mobile systems under high mechanical loads must possess both high strength and ductility. Achieving such multi-properties presents a fundamental challenge. Here, we solve this problem by combining multicomponent alloy design with atomic-scale chemistry tuning. We term the resultant material ‘Resinvar’ alloy, due to its invariable resistivity (148 μΩ·cm) over wide temperature ranges from room temperature to 723 K. The alloy also has high tensile strength (948 MPa) at large tensile elongation (53%). The distorted lattice, chemical short-range order and ordered coherent nanoprecipitates in the material enable the invariant resistivity via promoting temperature-independent inelastic electron scattering, and contribute to the excellent strength-ductility synergy by manipulating dislocation slip.

Abstract The far-from-equilibrium solidification during additive manufacturing often creates large residual stresses that induce solid-state cracking. Here we present a strategy to suppress solid-state cracking in an additively manufactured AlCrFe2Ni2 high-entropy alloy via engineering phase transformation pathway. We investigate the solidification microstructures formed during laser powder-bed fusion and directed energy deposition, encompassing a broad range of cooling rates. At high cooling rates (104−106 K/s), we observe a single-phase BCC/B2 microstructure that is susceptible to solid-state cracking. At low cooling rates (102−104 K/s), FCC phase precipitates out from the BCC/B2 matrix, resulting in enhanced ductility (~10 %) and resistance to solid-state cracking. Site-specific residual stress/strain analysis reveals that the ductile FCC phase can largely accommodate residual stresses, a feature which helps relieve residual strains within the BCC/B2 phase to prevent cracking. Our work underscores the value of exploiting the toolbox of phase transformation pathway engineering for material design during additive manufacturing.

Abstract Excellent castability, significantly refined microstructure, and good mechanical properties make eutectic high‐entropy alloys (EHEAs) a natural fit for rapid solidification processes, e.g., additive manufacturing. Previous investigations have focused on developing EHEAs through trial and error and mixing known binary eutectic materials. However, eutectic compositions obtained from near‐equilibrium conditions do not guarantee a fully eutectic microstructure under rapid solidifications. In this work, a thermodynamically guided high‐throughput framework is proposed to design EHEAs for rapid solidification. Empirical formulas derived from past experimental observations and thermodynamic computations are applied and considered phase growth kinetics under rapid solidification (skewed phase diagram). The designed alloy candidate, Co25.6Fe17.9Ni22.4Cr19.1Ta8.9Al6.1 (wt.%), contains nanostructured eutectic lamellar and shows a high Vickers hardness of 675 Hv. In addition to this specific composition, the alloy design toolbox enables the development of new EHEAs for rapid solidification without the limitation of previous knowledge.

Abstract Aluminum alloys play an important role in circular metallurgy due to their good recyclability and 95% energy gain when made from scrap. Their low density and high strength translate linearly to lower greenhouse gas emissions in transportation, and their excellent corrosion resistance enhances product longevity. The durability of Al alloys stems from the dense barrier oxide film strongly bonded to the surface, preventing further degradation. However, despite decades of research, the individual elemental reactions and their influence on the nanoscale characteristics of the oxide film during corrosion in multicomponent Al alloys remain unresolved questions. Here, we build up a direct correlation between the near-atomistic picture of the corrosion oxide film and the solute reactivity in the aqueous corrosion of a high-strength Al-Zn-Mg-Cu alloy. We reveal the formation of nanocrystalline Al oxide and highlight the solute partitioning between the oxide and the matrix and segregation to the internal interface. The sharp decrease in partitioning content of Mg in the peak-aged alloy emphasizes the impact of heat treatment on the oxide stability and corrosion kinetics. Through H isotopic labelling with deuterium, we provide direct evidence that the oxide acts as a trap for this element, pointing at the essential role of the Al oxide might act as a kinetic barrier in preventing H embrittlement. Our findings advance the mechanistic understanding of further improving the stability of Al oxide, guiding the design of corrosion-resistant alloys for potential applications.

The precipitation of grain boundary (GB) κ-carbides critically influences the damage-tolerance ability of high-Mn high-Al lightweight steels, particularly in harsh environments (cryogenic and H environments). The formation and growth behavior of these carbides thus need to be understood. In this work, we use atom probe tomography and four-dimensional scanning transmission electron microscopy to study the formation mechanisms of GB κ-carbides in a Fe-28Mn-8Al-1.3C (wt.%) steel that is aged at 550°C, a temperature at which GB κ-carbide formation is often believed to be delayed or avoided. We observe that the formation of GB κ-carbides results from spinodal decomposition of grain interior (GI) κ-carbides and their further interaction with GB planes rather than from heterogeneous GB nucleation, as has been formerly proposed. However, the subsequent growth of GB κ-carbides shows different kinetics in comparison to GI κ-carbides. The underlying reason and its implications for future microstructure design of such steels are discussed.

Abstract Fast growth of sustainable energy production requires massive electrification of transport, industry and households, with electrical motors as key components. These need soft magnets with high saturation magnetization, mechanical strength, and thermal stability to operate efficiently and safely. Reconciling these properties in one material is challenging because thermally-stable microstructures for strength increase conflict with magnetic performance. Here, we present a material concept that combines thermal stability, soft magnetic response, and high mechanical strength. The strong and ductile soft ferromagnet is realized as a multicomponent alloy in which precipitates with a large aspect ratio form a Widmanstätten pattern. The material shows excellent magnetic and mechanical properties at high temperatures while the reference alloy with identical composition devoid of precipitates significantly loses its magnetization and strength at identical temperatures. The work provides a new avenue to develop soft magnets for high-temperature applications, enabling efficient use of sustainable electrical energy under harsh operating conditions.

Abstract Hydrogen-based reduction of iron ores is the key technology for future sustainable ironmaking, to mitigate the CO2 burden from the steel industry, accounting for ~7–8% of all global emissions. However, using hydrogen as a reductant prompts concerns about hydrogen embrittlement in steel products. This raises the question of how much hydrogen remains from green ironmaking in the metal produced. We answer this question here by quantifying the amount of hydrogen in iron produced via two hydrogen-based ironmaking processes, namely, direct reduction and plasma smelting reduction. Results suggest no threat of hydrogen embrittlement resulting from using hydrogen in green steel production.

Abstract Grain boundaries, the two-dimensional defects between differently oriented crystals, tend to preferentially attract solutes for segregation. Solute segregation has a significant effect on the mechanical and transport properties of materials. At the atomic level, however, the interplay of structure and composition of grain boundaries remains elusive, especially with respect to light interstitial solutes like B and C. Here, we use Fe alloyed with B and C to exploit the strong interdependence of interface structure and chemistry via charge-density imaging and atom probe tomography methods. Direct imaging and quantifying of light interstitial solutes at grain boundaries provide insight into decoration tendencies governed by atomic motifs. We find that even a change in the inclination of the grain boundary plane with identical misorientation impacts grain boundary composition and atomic arrangement. Thus, it is the smallest structural hierarchical level, the atomic motifs, that controls the most important chemical properties of the grain boundaries. This insight not only closes a missing link between the structure and chemical composition of such defects but also enables the targeted design and passivation of the chemical state of grain boundaries to free them from their role as entry gates for corrosion, hydrogen embrittlement, or mechanical failure.

Abstract The purpose of this work is the development of a trained artificial neural network for surrogate modeling of the mechanical response of elasto-viscoplastic grain microstructures. To this end, a U-Net-based convolutional neural network (CNN) is trained using results for the von Mises stress field from the numerical solution of initial-boundary-value problems (IBVPs) for mechanical equilibrium in such microstructures subject to quasi-static uniaxial extension. The resulting trained CNN (tCNN) accurately reproduces the von Mises stress field about 500 times faster than numerical solutions of the corresponding IBVP based on spectral methods. Application of the tCNN to test cases based on microstructure morphologies and boundary conditions not contained in the training dataset is also investigated and discussed.

Abstract It has long been a norm that researchers extract knowledge from literature to design materials. However, the avalanche of publications makes the norm challenging to follow. Text mining (TM) is efficient in extracting information from corpora. Still, it cannot discover materials not present in the corpora, hindering its broader applications in exploring novel materials, such as high-entropy alloys (HEAs). Here we introduce a concept of “context similarity" for selecting chemical elements for HEAs, based on TM models that analyze the abstracts of 6.4 million papers. The method captures the similarity of chemical elements in the context used by scientists. It overcomes the limitations of TM and identifies the Cantor and Senkov HEAs. We demonstrate its screening capability for six- and seven-component lightweight HEAs by finding nearly 500 promising alloys out of 2.6 million candidates. The method thus brings an approach to the development of ultrahigh-entropy alloys and multicomponent materials.

We investigated the effects of interstitial N and C on the stacking fault energy (SFE) of an equiatomic CoCrNi medium entropy alloy. Results of computer modeling were compared to tensile deformation and electron microscopy data. Both N and C in solid solution increase the SFE of the face-centered cubic (FCC) alloy matrix at room temperature, with the former having a more significant effect by 240% for 0.5 at % N. Total energy calculations based on density functional theory (DFT) as well as thermodynamic modeling of the Gibbs free energy with the CALPHAD (CALculation of PHAse Diagrams) method reveal a stabilizing effect of N and C interstitials on the FCC lattice with respect to the hexagonal close-packed (HCP) CoCrNi-X (X: N, C) lattice. Scanning transmission electron microscopy (STEM) measurements of the width of dissociated ½ dislocations suggest that the SFE of CoCrNi increases from 22 to 42–44 mJ·m−2 after doping the alloy with 0.5 at. % interstitial N. The higher SFE reduces the nucleation rates of twins, leading to an increase in the critical stress required to trigger deformation twinning, an effect which can be used to design load-dependent strain hardening response.

Abstract Gas–solid reactions are important for many redox processes that underpin the energy and sustainability transition. The specific case of hydrogen‐based iron oxide reduction is the foundation to render the global steel industry fossil‐free, an essential target as iron production is the largest single industrial emitter of carbon dioxide. This perception of gas–solid reactions has not only been limited by the availability of state‐of‐the‐art techniques which can delve into the structure and chemistry of reacted solids, but one continues to miss an important reaction partner that defines the thermodynamics and kinetics of gas phase reactions: the gas molecules. In this investigation, cryogenic‐atom probe tomography is used to study the quasi in situ evolution of iron oxide in the solid and gas phases of the direct reduction of iron oxide by deuterium gas at 700°C. So far several unknown atomic‐scale characteristics are observed, including, D2 accumulation at the reaction interface; formation of a core (wüstite)‐shell (iron) structure; inbound diffusion of D through the iron layer and partitioning of D among phases and defects; outbound diffusion of oxygen through the wüstite and/or through the iron to the next free available inner/outer surface; and the internal formation of heavy nano‐water droplets at nano‐pores.

Abstract We have developed a deep-learning-based framework for understanding the individual and mutually combined contributions of different alloying elements and environmental conditions towards the pitting resistance of corrosion-resistant alloys. A fully connected deep neural network (DNN) was trained on previously published datasets on corrosion-relevant electrochemical metrics, to predict the pitting potential of an alloy, given the chemical composition and environmental conditions. Mean absolute error of 170 mV in the predicted pitting potential, with an R-square coefficient of 0.61 was obtained after training. The trained DNN model was used for multi-dimensional gradient descent optimization to search for conditions maximizing the pitting potential. Among environmental variables, chloride-ion concentration was universally found to be detrimental. Increasing the amounts of dissolved nitrogen/carbon was found to have the strongest beneficial influence in many alloys. Supersaturating transition metal high entropy alloys with large amounts of interstitial nitrogen/carbon has emerged as a possible direction for corrosion-resistant alloy design.

Production defects prevent many industrially important materials from being adopted by metal additive manufacturing. Here, the authors propose a universal thermodynamics-guided alloy design approach to assist the discovery of crack-free materials.

Abstract Iron making is the biggest single cause of global warming. The reduction of iron ores with carbon generates about 7% of the global carbon dioxide emissions to produce ≈1.85 billion tons of steel per year. This dramatic scenario fuels efforts to re‐invent this sector by using renewable and carbon‐free reductants and electricity. Here, the authors show how to make sustainable steel by reducing solid iron oxides with hydrogen released from ammonia. Ammonia is an annually 180 million ton traded chemical energy carrier, with established transcontinental logistics and low liquefaction costs. It can be synthesized with green hydrogen and release hydrogen again through the reduction reaction. This advantage connects it with green iron making, for replacing fossil reductants. the authors show that ammonia‐based reduction of iron oxide proceeds through an autocatalytic reaction, is kinetically as effective as hydrogen‐based direct reduction, yields the same metallization, and can be industrially realized with existing technologies. The produced iron/iron nitride mixture can be subsequently melted in an electric arc furnace (or co‐charged into a converter) to adjust the chemical composition to the target steel grades. A novel approach is thus presented to deploying intermittent renewable energy, mediated by green ammonia, for a disruptive technology transition toward sustainable iron making.

Mechanical twinning is difficult to trigger in face centered cubic alloys with high stacking fault energies (SFEs) under standard tensile loading. Here, the authors report high stress twinning in a bulk compositionally complex steel of very high SFE, enhancing the material’s mechanical performance.

Abstract A seamless and lossless transition of the constitutive description of the elastic response of materials between atomic and continuum scales has been so far elusive. Here we show how this problem can be overcome by using artificial intelligence (AI). A convolutional neural network (CNN) model is trained, by taking the structure image of a nanoporous material as input and the corresponding elasticity tensor, calculated from molecular statics (MS), as output. Trained with the atomistic data, the CNN model captures the size- and pore-dependency of the material’s elastic properties which, on the physics side, derive from its intrinsic stiffness as well as from surface relaxation and non-local effects. To demonstrate the accuracy and the efficiency of the trained CNN model, a finite element method (FEM)-based result of an elastically deformed nanoporous beam equipped with the CNN as constitutive law is compared with that obtained by a full atomistic simulation. The trained CNN model predicts the elasticity tensor in the test dataset with a root-mean-square error of 2.4 GPa (3.0% of the bulk modulus) when compared to atomistic calculations. On the other hand, the CNN model is about 230 times faster than the MS calculation and does not require changing simulation methods between different scales. The efficiency of the CNN evaluation together with the preservation of important atomistic effects makes the trained model an effective atomistically informed constitutive model for macroscopic simulations of nanoporous materials, optimization of nanostructures, and the solution of inverse problems.

Abstract The complex interplay between chemistry, microstructure, and behavior of many engineering materials has been investigated predominantly by experimental methods. Parallel to the increase in computer power, advances in computational modeling methods have resulted in a level of sophistication which is comparable to that of experiments. At the continuum level, one class of such models is based on continuum thermodynamics, phase-field methods, and crystal plasticity, facilitating the account of multiple physical mechanisms (multi-physics) and their interaction during microstructure evolution. This paper reviews the status of simulation approaches and software packages in this field and gives an outlook towards promising research directions.

Interstitials can substantially strengthen metals. Here the authors show a massive interstitial solid solution (MISS) approach enabling a model multicomponent alloy to achieve near-theoretical strength together with large deformability.

Common wisdom to improve ductility of bulk metallic glasses (BMGs) is to introduce local loose packing regions at the expense of strength. Here the authors enhance structural fluctuations of BMGs by introducing dense local packing regions, resulting in simultaneous increase of ductility and strength.

It is know from literature that small additions (

Wear-resistant metals have long been a pursuit of reducing wear-related energy and material loss. Here the authors present the ‘reactive wear protection’ strategy via friction-induced in situ formation of strong and deformable oxide nanocomposites on a surface.

Abstract We propose a deep neural network (DNN) as a fast surrogate model for local stress calculations in inhomogeneous non-linear materials. We show that the DNN predicts the local stresses with 3.8% mean absolute percentage error (MAPE) for the case of heterogeneous elastic media and a mechanical contrast of up to factor of 1.5 among neighboring domains, while performing 103 times faster than spectral solvers. The DNN model proves suited for reproducing the stress distribution in geometries different from those used for training. In the case of elasto-plastic materials with up to 4 times mechanical contrast in yield stress among adjacent regions, the trained model simulates the micromechanics with a MAPE of 6.4% in one single forward evaluation of the network, without any iteration. The results reveal an efficient approach to solve non-linear mechanical problems, with an acceleration up to a factor of 8300 for elastic-plastic materials compared to typical solvers.

The competition between the formation of different phases and their kinetics need to be clearly understood to make materials with on-demand and multifaceted properties. Here, the authors reveal, by a combination of complementary in situ techniques, the mechanism of a Cu-Zr-Al metallic glass’s high propensity for metastable phase formation, which is partially through a kinetic mechanism of Al partitioning.

The relationship between the strain rate and micro-scale deformation in metals is still poorly understood. Here the authors use discrete dislocation dynamics and molecular dynamics to establish a universal relationship between material strength, dislocation density, strain rate and dislocation mobility in fcc metals.

Abstract Segregation to grain boundaries affects their cohesion, corrosion, and embrittlement and plays a critical role in heterogeneous nucleation. In order to quantitatively study segregation and low-dimensional phase separation at grain boundaries, here, we apply a density-based phase-field model. The current model describes the grain-boundary thermodynamic properties based on available bulk thermodynamic data, while the grain-boundary-density profile is obtained using atomistic simulations. To benchmark the performance of the model, Mn grain-boundary segregation in the Fe–Mn system is studied. 3D simulation results are compared against atom probe tomography measurements conducted for three alloy compositions. We show that a continuous increase in the alloy composition results in a discontinuous jump in the segregation isotherm. The jump corresponds to a spinodal phase separation at grain boundary. For alloy compositions above the jump, we reveal an interfacial transient spinodal phase separation. The transient spinodal phenomenon opens opportunities for knowledge-based microstructure design through the chemical manipulation of grain boundaries. The proposed density-based model provides a powerful tool to study thermodynamics and kinetics of segregation and phase changes at grain boundaries.

Anion vacancies are a hurdle for technologies based on chalcogenide semiconductors and topological insulators. Even at room temperature, oxidation and cyanide etching can lead to selenium vacancies in CuInSe2 photovoltaic material but suitable post deposition treatments can mitigate their effect.

Strong and ductile materials with resistance to both corrosion and hydrogen embrittlement remain rare and yet are essential for hydrogen-propelled industries. Here, the authors show that a CoNiV medium-entropy alloy with face-centered cubic structure fulfils all the above criteria.

Adding minute amounts of rhenium to Ni-based single crystal superalloys extends their high temperature performance in engines, but the reasons behind that are still unclear. Here, the authors combine high resolution imaging and modelling to show that rhenium enriches and slows down partial dislocations to improve creep performance.

Numerous metallurgical and materials science applications depend on quantitative atomic-scale characterizations of environmentally-sensitive materials and their transient states. Studying the effect upon materials subjected to thermochemical treatments in specific gaseous atmospheres is of central importance for specifically studying a material’s resistance to certain oxidative or hydrogen environments. It is also important for investigating catalytic materials, direct reduction of an oxide, particular surface science reactions or nanoparticle fabrication routes. This manuscript realizes such experimental protocols upon a thermochemical reaction chamber called the "Reacthub" and allows for transferring treated materials under cryogenic & ultrahigh vacuum (UHV) workflow conditions for characterisation by either atom probe or scanning Xe+/electron microscopies. Two examples are discussed in the present study. One protocol was in the deuterium gas charging (25 kPa D2 at 200°C) of a high-manganese twinning-induced-plasticity (TWIP) steel and characterization of the ingress and trapping of hydrogen at various features (grain boundaries in particular) in efforts to relate this to the steel’s hydrogen embrittlement susceptibility. Deuterium was successfully detected after gas charging but most contrast originated from the complex ion FeOD+ signal and the feature may be an artefact. The second example considered the direct deuterium reduction (5 kPa D2 at 700°C) of a single crystal wüstite (FeO) sample, demonstrating that under a standard thermochemical treatment causes rapid reduction upon the nanoscale. In each case, further studies are required for complete confidence about these phenomena, but these experiments successfully demonstrate that how an ex-situ thermochemical treatment can be realised that captures environmentally-sensitive transient states that can be analysed by atomic-scale by atom probe microscope.

Designing complex concentrated alloys with targeted properties for high performance remains challenging because of their complex local atomic environments. Here, the authors show how to engineer atomic-level pressure to customize complexity-induced properties such as solid-solution strengthening.

Hydrogen contamination in metals during sample preparation for high-resolution microscopy remains a challenge, especially when hydrogen itself is being investigated. Here, the authors show that using cryogenic milling significantly reduces hydrogen pick-up during sample preparation of titanium and titanium alloys.

Summary: Mass spectrometry is a widespread approach used to work out what the constituents of a material are. Atoms and molecules are removed from the material and collected, and subsequently, a critical step is to infer their correct identities based on patterns formed in their mass-to-charge ratios and relative isotopic abundances. However, this identification step still mainly relies on individual users' expertise, making its standardization challenging, and hindering efficient data processing. Here, we introduce an approach that leverages modern machine learning technique to identify peak patterns in time-of-flight mass spectra within microseconds, outperforming human users without loss of accuracy. Our approach is cross-validated on mass spectra generated from different time-of-flight mass spectrometry (ToF-MS) techniques, offering the ToF-MS community an open-source, intelligent mass spectra analysis. The bigger picture: Time-of-flight mass spectrometry (ToF-MS) is a mainstream analytical technique widely used in biology, chemistry, and materials science. ToF-MS provides quantitative compositional analysis with high sensitivity across a wide dynamic range of mass-to-charge ratios. A critical step in ToF-MS is to infer the identity of the detected ions. Here, we introduce a machine-learning-enhanced algorithm to provide a user-independent approach to performing this identification using patterns from the natural isotopic abundances of individual atomic and molecular ions, without human labeling or prior knowledge of composition. Results from several materials and techniques are compared with those obtained by field experts. Our open-source, easy-to-implement, reliable analytic method accelerates this identification process. A wide range of ToF-MS-based applications can benefit from our approach, e.g., hunting for patterns of biomarkers or for contamination on solid surfaces in high-throughput data.

Quantum mechanically guided materials design has been used to predict the mechanical property trends in crystalline materials. Thereby, the identification of composition-structure-property relationships is enabled. However, quantum mechanics based design guidelines and material selection criteria for ultra-strong metallic glasses have been lacking. Hence, based on an ab initio model for metallic glasses in conjunction with an experimental high-throughput methodology geared toward revealing the relationship between chemistry, topology and mechanical properties, we propose principles for the design of tough as well as stiff metallic glasses. The main design notion is that a low fraction of hybridized bonds compared to the overall bonding in a metallic glass can be used as a criterion for the identification of damage-tolerant metallic glass systems. To enhance the stiffness of metallic glasses, the bond energy density must be increased as the bond energy density is the origin of stiffness in metallic glasses. The thermal expansion, which is an important glass-forming identifier, can be predicted based on the Debye-Grüneisen model.

Abstract Millions worldwide suffer from arthritis of the hips, and total hip replacement is a clinically successful treatment for end‐stage arthritis patients. Typical hip implants incorporate a cobalt alloy (Co–Cr–Mo) femoral head fixed on a titanium alloy (Ti‐6Al‐4V) femoral stem via a Morse taper junction. However, fretting and corrosion at this junction can cause release of wear particles and metal ions from the metallic implant, leading to local and systemic toxicity in patients. This study is a multiscale structural‐chemical investigation, ranging from the micrometer down to the atomic scale, of the underlying mechanisms leading to metal ion release from such taper junctions. Correlative transmission electron microscopy and atom probe tomography reveals microstructural and compositional alterations in the subsurface of the titanium alloy subjected to in vitro gross‐slip fretting against the cobalt alloy. Even though the cobalt alloy is comparatively more wear‐resistant, changes in the titanium alloy promote tribocorrosion and subsequent degradation of the cobalt alloy. These observations regarding the concurrent occurrence of electrochemical and tribological phenomena are vital to further improve the design and performance of taper junctions in similar environments.

High fracture toughness is crucial for the application of metallic glasses as structural materials to avoid catastrophic failure of the material in a brittle manner. One fingerprint for fracture toughness in metallic glasses is the fraction of hybridized bonds, which is affected by alloying Pd57.4Al23.5Y7.8M11.3 with M = Fe, Ni, Co, Cu, Os, Ir, Pt, and Au. It is shown that experimental fracture toughness data is correlated to the fraction of hybridized bonds which scale with the localized bonds at the Fermi level. Thus, the localized bonds at the Fermi level are utilized quantitatively as a measure for fracture toughness. Based on ab initio calculations, the minimum fraction of hybridized bonds was identified for Pd57.4Al23.5Y7.8Ni11.3. According to the ansatz that the crystal orbital overlap population at the Fermi level scales with fracture toughness, for Pd57.4Al23.5Y7.8Ni11.3 a value of around 95 ± 20 MPa·m0.5 is predicted quantitatively for the first time. Consistent with this prediction, in micro-mechanical beam bending experiments Pd57.4Al23.5Y7.8Ni11.3 thin films show pronounced plasticity and absence of crack growth. Keywords: Metallic glass, Ab initio, Fracture toughness, Chemical bonding

Fe–TiB2 metal matrix composites, termed high modulus steels, have great potential for lightweight design applications due to their high stiffness/density ratio. However, the observed embrittlement, caused by the TiB2 particles, critically limits application of these steels. Experimental studies to identify the influence of particle microstructure on ductility and toughness are complex in view of the multitude of parameters affecting microstructural damage. We therefore pursue instead an integrated computational materials engineering approach to gain understanding and derive guidelines for optimizing the particle microstructure and thus improve the mechanical properties, particularly the damage tolerance of these high modulus steels. Key microstructural parameters such as particle clustering degree, size and volume fraction were investigated. Model geometries were statistically and systematically generated with varied particle configurations from random to clustered distributions. Simulations were performed using a crystal plasticity Fast Fourier transformation method coupled with a novel phase field damage model. The influence of particle configuration on damage initiation and evolution was evaluated from the simulation results, and it was observed that microstructures with homogeneous particle distributions of 7 to 15 vol% TiB2, devoid of large TiB2 particles stemming from primary solidification, appear most favorable for obtaining high modulus steels with optimized mechanical properties. Keywords: Metal matrix composites, Damage simulation, Particle damage, Phase field method, Spectral method

Sodium doping is necessary to achieve high performance in polycrystalline chalcopyrite solar cells, but retards gallium interdiffusion, and thus efficiency optimisation. Here, Colombara et al. show that in contrast to the polycrystalline case, sodium accelerates atomic interdiffusion in monocrystalline samples.

Research in new alloy compositions and treatments may allow the increased strength of mass-produced, intricately shaped parts. Here authors introduce a superplastic medium manganese steel which has an inexpensive lean chemical composition and which is suited for conventional manufacturing processes.

A [211]-oriented niobium single crystal was deformed by equal channel angular pressing (ECAP) at room temperature using the route Bc to a total strain of 9.2. A sharp cube texture develops after ECAP processing. The deformed samples were annealed in vacuum from 400ºC (673 K) to 900ºC (1173 K) for 1 h to evaluate their microstructural stability. Scanning electron microscopy (SEM) was used to image the microstructures of as-deformed and annealed specimens. Electron backscatter diffraction (EBSD) was employed to determine the respective microtextures before and after annealing. Coarsening of the microstructure occurs at a maximum rate at 550ºC (823 K) due to discontinuous recrystallization. Normal grain growth replaces discontinuous recrystallization as the main coarsening mechanism above 700ºC (973 K).

Abstract Mg is the most important lightweight engineering alloy enabling future weight-reduced and fuel-saving engineering solutions. Yet, Mg is soft. Long-period stacking ordered (LPSO) structures in Mg alloys have unique crystal structures, characterized by both complex chemical and stacking order. They are essential for strengthening of Mg alloys. The formation mechanism of these LPSO structures is still under discussion. Here we report that Y/Zn enriched Guinier-Preston (GP) zones observed in a lean Mg-Y-Zn model alloy are precursors of early stage LPSO structures. We provide evidence of a new type of phase transformation mechanism which comprises the diffusional formation of Y/Zn enriched GP zones and their subsequent shear transformation into LPSO building blocks. The mechanism constitutes a new type of coupled diffusional-displacive phase formation sequence which may also be applicable to other alloy systems.

Abstract We report on the oxidation behavior of AlN/CrN multilayers at 900 °C, deposited by radio frequency magnetron sputtering. It is shown that oxidation in this system is controlled by diffusion of Cr towards the surface and formation of Cr2O3. Cr diffusion is found to mainly occur along grain boundaries. Thus, coherent cubic AlN/CrN multilayer regions with coarse columnar grain structures are found to be oxidation resistant, whereas regions decomposed into hexagonal AlN/cubic CrN are prone to oxidation.

The plastic co-deformation behavior at the homophase interfaces between the hard nanotwinned grain inclusions and the soft recrystallized matrix grains in a duplex-microstructured AISI 316L austenitic stainless steel is examined through the analysis of long-range orientation gradients within the matrix grains by electron backscatter diffraction and transmission electron microcopy. Our analysis reveals that the mechanical accommodation of homophase interfaces until a macroscopic strain of 22% is realized within a small area of soft grains (about four grains) adjacent to the homophase interface. The activation of deformation twinning in the first two grain layers results in the occurrence of a ‘hump’ in the orientation gradient profile. We ascribe this effect to the role of deformation twinning on the generation of geometrically necessary dislocations. The smooth profile of the orientation gradient amplitude within the first 10 grain layers indicates a gradual plastic accommodation of the homophase interfaces upon straining. As a consequence, damage nucleation at such interfaces is impeded, resulting in an enhanced ductility of the single phase duplex-microstructured steel.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Dual-phase high-entropy alloys (DP-HEAs) with transformation induced plasticity (TRIP) have an excellent strength-ductility combination. To reveal their strain-rate sensitivity and hence further understand the corresponding deformation mechanisms, we investigated the tensile behavior and microstructural evolution of a typical TRIP-DP-HEA (Fe50Mn30Co10Cr10, at. %) under different strain rates (i.e., 5 × 10−3 s−1, 1 × 10−3 s−1, 5 × 10−4 s−1 and 1 × 10−4 s−1) at room temperature. The strain rate range was confined to this regime in order to apply the digital image correlation technique for probing the local strain evolution during tensile deformation at high resolution and to correlate it to the microstructure evolution. Grain size effects of the face-centered cubic (FCC) matrix and the volume fractions of the hexagonal-close packed (HCP) phase prior to deformation were also considered. The results show that within the explored strain rate regime the TRIP-DP-HEA has a fairly low strain rate sensitivity parameter within the range from 0.004 to 0.04, which is significantly lower than that of DP and TRIP steels. Samples with varying grain sizes (e.g., ~2.8 and 38 μm) and starting HCP phase fractions (e.g., ~25 and 72%) at different strain rates show similar deformation mechanisms, i.e., dislocation plasticity and strain-induced transformation from the FCC matrix to the HCP phase. The low strain rate sensitivity is attributed to the observed dominant displacive transformation mechanism. Also, the coarse-grained alloy samples with a very high starting HCP phase fraction (~72%) prior to deformation show very good ductility with a total elongation of ~60%, suggesting that both, the initial and the transformed HCP phase in the TRIP-DP-HEA are ductile and deform further via dislocation slip at the different strain rates which were probed.