High-entropy Alloy Composites Research Articles

"One-Stone, Two-Birds": Zinc-Rich Metal-Organic Frameworks as Precursors for High-Entropy Zn-Air Battery Electrocatalysts with Hierarchical Pore Structures.

The active sites of inexpensive transition metal electrocatalysts are sparse and singular, thus high-entropy alloys composed of non-precious metals have attracted considerable attention due to their multi-component synergistic effects. However, the facile synthesis of high-entropy alloy composites remains a challenge. Herein, we report a "one-stone, two-birds" method utilizing zinc (Zn)-rich metal-organic frameworks as precursors, by virtue of the low boiling point of Zn (907 °C) and its high volatility in alloys, high-entropy alloy carbon nanocomposite with a layered pore structure was ultimately synthesized. The experimental results demonstrate that the volatilization of zinc can prevent metal agglomeration and contribute to the formation of uniformly dispersed high-entropy alloy nanoparticles at slower pyrolysis and cooling rates. Simultaneously, the volatilization of Zn plays a crucial role in creating the hierarchically porous structure. Compared to the zinc-free HEA/NC-1, the HEA/NC-5 derived from the precursor containing 0.8 Zn exhibit massive micropores and mesopores. The resulting nanocomposites represent a synergistic effect between highly dispersed metal catalytic centers and hierarchical adsorption sites, thus achieving excellent electrocatalytic oxygen reduction performance with low catalyst loading compared to commercial Pt/C. This convenient zinc-rich precursor method can be extended to the production of more high-entropy alloys and various application fields.

Influence of HEA reinforcement on pulse electric current sintered Aluminium Composites: Microstructure, Density, and nanomechanical properties

This study investigates how different compositions of high-entropy alloys (HEAs), known for their strength and thermal stability, affect the microstructure, density, and nanomechanical properties of aluminium matrix composites produced by pulse electric current sintering (PECS). Commercial aluminium was reinforced with 5%, 7%, and 10% HEA and sintered using PECS. Microstructural analysis showed improved particle bonding and dispersion, resulting in enhanced mechanical properties. Although relative density slightly decreased with higher HEA content, there was a significant enhancement in nanohardness and elastic modulus. The composites exhibited improved resistance to plastic deformation and increased stiffness, thus, highlighting the effectiveness of HEA reinforcement in optimizing aluminium composites for high-strength, lightweight, and advanced engineering applications. These findings offer valuable insights for developing next-generation materials suited to demanding engineering environments.

Machine-learning synergy in high-entropy alloys: A review

High-entropy alloys (HEAs) have attracted significant attention because of their exceptional mechanical properties and potential for discovering new compositions. However, owing to their complex chemical makeup, understanding the underlying physical mechanisms and designing novel alloys through traditional theoretical and computational methods are challenging. Machine learning (ML) offers a promising solution to address these challenges. This review begins with a discussion of the critical data preprocessing techniques required for effective ML applications in the HEA domain. Subsequently, this review discusses various ML models, including supervised and unsupervised algorithms, focusing on guiding readers through algorithm selection and model evaluation. These fundamental aspects are essential for understanding the complexities of applying ML in HEA research. Further, this review highlights various successful applications of ML in HEA research. These include optimisation of the alloy composition, processing parameters, and microstructural characteristics to enhance the mechanical properties. In addition, this review examines the use of ML for performance-driven reverse engineering of HEA compositions, enabling the rapid identification of new high-performance alloy designs. The novelty of this review is its comprehensive and integrative approach to ML applications in HEAs. Unlike previous studies that focused on specific ML techniques or isolated use cases, this review explores the transformative potential of ML across the entire HEA research landscape. By covering the full spectrum from fundamental data preprocessing and model selection to a diverse range of practical applications, this review provides insights into how ML can accelerate the discovery and development of high-performance HEAs. This multifaceted perspective covers the synergistic interplay between various ML methodologies and their impact on HEA research, opening up new opportunities for innovation that may not have been fully explored in more specialized studies. Considering the extensive studies on ML discussed in this review, it can be concluded that ML has revolutionised the design and development of HEAs. By optimising alloy composition, processing parameters, and microstructural characteristics, ML-driven approaches have unlocked the potential for engineering high-performance HEAs with tailored mechanical properties. Moreover, the use of ML in performance-driven reverse engineering has enabled the rapid identification of promising HEA compositions, thereby accelerating the discovery of novel materials.

Strengthening of mechanical and tribological properties in CrFeCuMnNi high entropy alloy through dispersion of TiO2 reinforcement via powder metallurgy processes

The present study reports the effect of TiO2 reinforcement on the phase, microstructure, mechanical and tribological properties of CrFeCuMnNi/TiO2 high entropy alloy composites (HEACs) processed by powder metallurgy. The results reveal the formation of single-phase face-centered cubic (FCC) structure after 30 h of milling in the original HEA, while TiO2 peaks in the HEACs indicate uniform distribution. After sintering, multiple phases including FCC, Cr-rich, Cr2O3 were observed. The hardness of HEAs increased from 330±10 to 440±10 HV and compressive yield strength increased from 480 to 760 MPa while scarifying the strain from 43.5 to 21 % with TiO2 content. The addition of TiO2 content slightly reduces the coefficient of friction and specific wear rate decreased by 45.16 % due to increased hardness and formation of stable oxide layer on the worn surface inhibits plastic deformation. The addition of TiO2 reduces the dominance of abrasive and adhesive wear and enhances the strength of HEACs.

Microstructures, physical and corrosion behavior of NiCoFeCu high-entropy alloy nanocomposite coatings electro-co-deposited with nano-Si3N4 particles

High-entropy alloys (HEAs) have gained increasing attention over the decades, owing to their distinctive properties. Electrodeposition stands out as a cost-effective and convenient method for producing diverse types of HEAs. However, there's little attention paid to fabricating ceramic-enhanced HEA composites. In this study, NiFeCoCu/Si3N4 HEA nanocomposites were electro-co-deposited in an aqueous solution with varying loadings of Si3N4 nanoparticles and current densities. The morphological, elemental, and phase-structure characteristics were investigated using scanning electron microscopy (SEM), energy dispersive X-ray spectroscopy (EDX), and X-ray diffraction (XRD) techniques. The Vickers-microhardness measurements, reciprocal wear, and electrochemical tests were conducted to evaluate the physical and anti-corrosion performance of the composite coatings. The findings indicate that the incorporation of Si3N4 nanoparticles not only changes the surface morphologies, compositions, and textures of the HEA matrix but also significantly enhances both the wear and anti-corrosion performance in artificial water by refining the microstructures and reducing the defects of the composite coatings. The HEA-3 g/L-Si3N4-40 mA/cm2 and HEA-6 g/L-Si3N4-40 mA/cm2 composite coatings demonstrate the best wear performance and anti-corrosion properties among the samples investigated, respectively. The present study shows a significant improvement in the mechanical and anti-corrosion properties of HEA coatings by incorporation of Si3N4 nanoparticles, which is a very promising approach for the surface protection of engineering materials used in corrosive and frictional-working conditions.

Radiation Resistance of High-Entropy Alloys CoCrFeNi and CoCrFeMnNi, Sequentially Irradiated with Kr and He Ions.

This work studied the effect of sequential irradiation by krypton and helium ions at room temperature on the composition and structure of CoCrFeNi and CoCrFeMnNi high-entropy alloys (HEAs). Irradiation of the HEAs by 280 keV Kr14+ ions up to a fluence of 5 × 1015 cm-2 and 40 keV He2+ ions up to a fluence of 2 × 1017 cm-2 did not alter their elemental distribution and constituent phases. Blisters formed on the nickel surface after sequential irradiation, where large blisters had an average diameter of 3.8 μm. The lattice parameter of the (Co, Cr, Fe and Ni) and (Co, Cr, Fe, Mn and Ni) solid solutions increased by 0.17% and 0.37% after sequential irradiation, respectively. Irradiation by Kr ions led to a decrease in tensile macrostresses in the HEAs in the region of krypton ion implantation (Region I) and the formation of compressive macrostresses in the region behind the peak of implanted krypton (Region II). Sequential irradiation formed large compressive stresses in Ni and HEAs equal to -131.5 MPa, -300 MPa and -613.5 MPa in Ni, CoCrFeNi and CoCrFeMnNi, respectively, in the Region II. Irradiation by krypton ions decreased the dislocation density by 1.6-2.3 times, and irradiation with helium ions increased it by 11-15 times relative to unirradiated samples for CoCrFeNi and CoCrFeMnNi, respectively. Sequentially irradiated CoCrFeMnNi HEA had higher macrostresses and dislocation density than CoCrFeNi.

Laser clad CoCrFeNiTixNby hypoeutectic high-entropy alloy coating: Effects of Ti and Nb content on mechanical, wear and corrosion properties

High-entropy alloys (HEAs) exhibit significant potential for advanced wear and corrosion-resistant applications. This study investigates the influence of Ti and Nb doping on the microstructure, phase composition, and properties of CoCrFeNiTixNby HEAs synthesized using high-speed laser cladding (HLC). The results demonstrate that increasing Ti and Nb content transforms the coating structure from a face-centered cubic (FCC) phase to a hybrid structure comprising FCC, body-centered cubic (BCC), and Laves phases, leading to a linear enhancement in surface hardness. The incorporation of Ti and Nb not only promotes the preferred orientation of the FCC phase (111) crystal plane but also significantly enhances tensile strength, though this comes at the expense of reduced plasticity. Specifically, the CoCrFeNiTi0.6Nb0.15 coating attains an ideal balance between strength and ductility, with a tensile strength of 1641.8 MPa and a tensile strain of 15.9 %, achieved through the formation of a eutectic structure. This coating also exhibits superior wear resistance and outstanding corrosion resistance, which are attributed to the stability of the passivation film, reinforced by the (111) crystal plane and high-density dislocations. These findings provide both theoretical and empirical foundations for the design of high-performance HEAs, underscoring their potential in industrial applications requiring robust wear and corrosion resistance.

Attaining exceptional wear resistance in an in-situ ceramic phase reinforced NbMoWTa refractory high entropy alloy composite by Spark plasma sintering

The refractory high-entropy alloys (RHEAs) exhibit great potential as structural components for aerospace equipment. However, their lack of wear resistance and increased coefficient of friction at room temperature (RT) impose limitations on their practical applications. Therefore, further enhancements are required to improve their friction and wear properties under RT. In this context, the development of NbMoWTa(h-BN)x RHEA ceramic composites in this work offers a viable solution to address this issue. Experimental results demonstrate that the addition of h-BN leads to the in-situ generation of (Nb,Ta)N/(Nb,Ta)2N and (Nb,Ta)B2 ceramic phases, significantly enhancing the hardness and wear resistance of the composites. The wear rate of NbMoWTa(h-BN)0.5 reaching as low as 1.32 × 10−8 mm3/Nm, which is four orders of magnitude lower than that of the RHEA. The NbMoWTa RHEA exhibits significant adhesive wear, which can be effectively mitigated in composites through the uniform dispersion of ceramic phase particles with lower mean free path. The abrasive particles primarily interact with the hard strengthening phase, effectively inhibiting plastic deformation in their vicinity. Consequently, the reduced mean free path between the ceramic phases limits the likelihood of metal matrix removal. Subsequently, aided by the presence of ceramic phases, the spontaneous formation of protective third bodies further inhibit surface material removal and ultimately ensures exceptional wear resistance.

Structural Changes in High-Entropy Alloys CoCrFeNi and CoCrFeMnNi, Irradiated by He Ions at a Temperature of 700 °C.

High-entropy alloys (HEA) are promising structural materials that will successfully resist high-temperature irradiation with helium ions and radiation-induced swelling in new generations of nuclear reactors. In this paper, changes in the elemental and phase composition, surface morphology, and structure of CoCrFeNi and CoCrFeMnNi HEAs irradiated with He2+ ions at a temperature of 700 °C were studied. Structural studies were mainly conducted using the X-ray diffraction method. The formation of a porous surface structure with many microchannels (open blisters) was observed. The average diameter of the blisters in CoCrFeMnNi is around 1.3 times smaller than in CoCrFeNi. It was shown that HEAs' elemental and phase compositions are stable under high-temperature irradiation. It was revealed that, in the region of the peak of implanted helium, high-temperature irradiation leads to the growth of tensile macrostresses in CoCrFeNi by 3.6 times and the formation of compressive macrostresses (-143 MPa) in CoCrFeMnNi; microstresses in the HEAs increase by 2.4 times; and the dislocation density value increases by 4.3 and 7.5 times for CoCrFeNi and CoCrFeMnNi, respectively. The formation of compressive macrostresses and a higher value of dislocation density indicate that the CoCrFeMnNi HEA tends to have greater radiation resistance compared to CoCrFeNi.

Single-Phase L10-Ordered High Entropy Thin Films with High Magnetic Anisotropy.

The vast high entropy alloy (HEA) composition space is promising for discovery of new material phases with unique properties. This study explores the potential to achieve rare-earth-free high magnetic anisotropy materials in single-phase HEA thin films. Thin films of FeCoNiMnCu sputtered on thermally oxidized Si/SiO2 substrates at room temperature are magnetically soft, with a coercivity on the order of 10Oe. After post-deposition rapid thermal annealing (RTA), the films exhibit a single face-centered-cubic phase, with an almost 40-fold increase in coercivity. Inclusion of 50 at.% Pt in the film leads to ordering of a single L10 high entropy intermetallic phase after RTA, along with high magnetic anisotropy and 3 orders of magnitude coercivity increase. These results demonstrate a promising HEA approach to achieve high magnetic anisotropy materials using RTA.

Wireless driven and self-sensing flexible gripper based on piezoelectric elastomer/high-entropy alloy magnetoelectric composite

The mechanical gripper shows significant importance as an alternative to manual work, but presents certain limitations in its gas or electric power with complex wire connection, presents limited available area and loud working noise, and lacks sensing function for feedback to the environment. In the present work, a novel magnetically driven and self-sensing flexible gripper was prepared based on piezoelectric elastomer and high-entropy alloy composite. The high-entropy alloy is prepared as FeCoNi(AlGa)0.5 to realize high permeability, while the piezoelectric elastomer is synthesized with superior elasticity and low modulus. The incorporation of FeCoNi(AlGa)0.5 into the piezoelectric elastomer exhibits high flexibility, magnetostriction performances and piezoelectric responses, reaching maximum strain of 1534.5 %, maximum magnetostrictive coefficient of 320 ppm and maximum piezoelectric response of 0.28 V/N. Meanwhile, the flexible gripper achieves synchronous wireless driven gripping function with a maximum lifting ratio to dead load over 41.38, and self-sensing function of target physical size with accuracy over 95 %. The flexible gripper shows maximum work power as low as 5 W. Thus, the magnetically driven and self-sensing flexible gripper shows great potential in intelligent industrial equipment.

Design of Experiment Is All You Need: Utilizing AI for High-Entropy Alloy Water Splitting Catalyst

High-entropy alloy (HEA) is a new group of material that have excited both fields of electrochemistry and computational chemistry, based on its intriguing concept and outstanding catalytic activity. Nevertheless, there exists a clear limitation to the 'trial-and-error' based research: its compositional space is too large. Therefore, AI-based high-throughput research is considered as an efficient way to optimize the composition of HEA electrocatalysts. To fully utilize AI's potential, the quality of data and rational Design of Experiment (DoE) is of importance. In this regard, we have 1) developed an automated one-step synthesis method of HEA with liquid handler and 2) made machine learning model with Gaussian process (GP) regression to minimize the overpotential (fig. 1a).DoE helps developing a new experiment by categorizing the variables and steps that are necessary. First step of DoE is to start with listing the variables. For the one-step synthesis of HEAs, independent variable was the composition of each elements, where the control variables were:A. Solvent (Amine/ethanol)B. Addition of CNT (O/X)C. Reaction temperature (high/low)D. Reaction time (long/short)E. Concentration of solution (5 mM / 10 mM)Each of control variables have two options and the selection is represented by writing an alphabet. For example, if amine (A) and high reaction temperature (C) condition was selected while the second option is used for the rest, it is written as AC. Based on confounding method theory, by conducting AC experiment, we also get information about BDE experiment by assuming a negative correlation to each other. It is written as: AC = -BDE. Similarly, an equivalent combinatorial list can be made for every possible combination (=25) , while the number of experiment is reduced from 32 to 16. Among these combinations, ABCE was found to be optimal synthesis condition (fig.1b).Afterward, we searched the optimal composition of HEAs, starting with grid searching the six-dimensional compositional field. MnFeCoNiCuMoPdPt - octonary alloy was the targeted alloy where the compositions of noble metal were fixed. Binomial distribution design was used to make the distance between neighboring points in 6-dimension compositional space equal (fig. 1c-d). 8C3 = 56 combinations were selected and consequently, the corresponding amounts of precursor solutions were transferred to 56 heat-inert vials and underwent heat treatment of 200 °C. Overpotential of 56 samples were measured and simple GP regression model was constructed based on these datasets.GP regression is composed of two parts: exploration and exploitation. During exploration, the optimal combination is recommended by searching the point that has the largest deviation. Interestingly, by comparing the Shapley Additive explanations (SHAP) value of each elements, it is clearly proved that Cu was harmful and Ni was beneficial to water splitting performance. Thus, Cu was screened out during the end of the exploration step. After finishing exploration, the next combination was recommended by searching near the point with least overpotential (exploitation step). By using GP regression, the optimal composition among HEAs was discovered in less than 200 experiments. It showed the overpotential less than 30mV for HER and less than 250mV for OER, making the overall water-splitting potential less than 1.5 V. In summary, a GP regression model showed synergistic effect with DoE, and HEA with the optimal composition showed an outstanding water splitting performance, reducing overall overpotential by 50% compared to the first step. Figure 1

Microstructure and mechanical properties of ZrB2 ceramic particle reinforced AlCoCrFeNi high entropy alloy composite materials prepared by spark plasma sintering

In this study, xZrB2-AlCoCrFeNi (x = 0,5,10,15) (wt%) based high entropy alloy (HEA) composites were prepared by Spark Plasma Sintering (SPS). The influence of ceramic particle ZrB2 content on the densification behavior, microstructural evolution, and mechanical properties of the composites was investigated through scanning electron microscopy, X-ray diffraction, and mechanical performance testing. The results indicate that the HEA composites undergo a phase transformation, from (FCC + BCC + B2) structure to (FCC + BCC + B2+Laves) structure with the addition of ZrB2. The incorporation of ZrB2 facilitates the emergence of the Cr precipitate phase, and the grain size of this phase enlarges as the ZrB2 content rises. Additionally, when the ZrB2 content is 10 %, the maximum compressive strength reached 2071 MPa. Furthermore, at the ZrB2 content of 15 %, the composite material achieves a maximum densification of 99.13 % and a maximum hardness of 1222.2 HV.

Uncovering Nanoindention Behavior of Amorphous/Crystalline High-Entropy-Alloy Composites.

Amorphous/crystalline high-entropy-alloy (HEA) composites show great promise as structural materials due to their exceptional mechanical properties. However, there is still a lack of understanding of the dynamic nanoindentation response of HEA composites at the atomic scale. Here, the mechanical behavior of amorphous/crystalline HEA composites under nanoindentation is investigated through a large-scale molecular dynamics simulation and a dislocation-based strength model, in terms of the indentation force, microstructural evolution, stress distribution, shear strain distribution, and surface topography. The results show that the uneven distribution of elements within the crystal leads to a strong heterogeneity of the surface tension during elastic deformation. The severe mismatch of the amorphous/crystalline interface combined with the rapid accumulation of elastic deformation energy causes a significant number of dislocation-based plastic deformation behaviors. The presence of surrounding dislocations inhibits the free slip of dislocations below the indenter, while the amorphous layer prevents the movement or disappearance of dislocations towards the substrate. A thin amorphous layer leads to great indentation force, and causes inconsistent stacking and movement patterns of surface atoms, resulting in local bulges and depressions at the macroscopic level. The increasing thickness of the amorphous layer hinders the extension of shear bands towards the lower part of the substrate. These findings shed light on the mechanical properties of amorphous/crystalline HEA composites and offer insights for the design of high-performance materials.

On exploiting nonparametric kernel-based probabilistic machine learning over the large compositional space of high entropy alloys for optimal nanoscale ballistics

The large compositional space of high entropy alloys (HEA) often presents significant challenges in comprehensively deducing the critical influence of atomic composition on their mechanical responses. We propose an efficient nonparametric kernel-based probabilistic computational mapping to obtain the optimal composition of HEAs under ballistic conditions by exploiting the emerging capabilities of machine learning (ML) coupled with molecular-level simulations. Compared to conventional ML models, the present Gaussian approach is a Bayesian paradigm that can have several advantages, including small training datasets concerning computationally intensive simulations and the ability to provide uncertainty measurements of molecular dynamics simulations therein. The data-driven analysis reveals that a lower concentration of Ni with a higher concentration of Al leads to higher dissipation of kinetic energy and lower residual velocity, but with higher penetration depth of the projectile. To deal with such conflicting computationally intensive functional objectives, the ML-based simulation framework is further extended in conjunction with multi-objective genetic algorithm for identifying the critical elemental compositions to enhance kinetic energy dissipation with minimal penetration depth and residual velocity of the projectile simultaneously. The computational framework proposed here is generic in nature, and it can be extended to other HEAs with a range of non-aligned multi-physical property demands.

Correlating Nitrate Adsorption with the Local Environments of FeCoNiCuZn High-Entropy Alloy Catalysts Using Machine Learning.

The electrocatalytic nitrate reduction to ammonia holds significant values for water remediations and energy applications, which quests for the development of highly effective catalysts with considerable stability and selectivity. Recently, high-entropy alloys (HEAs) are attracting growing attention for electrocatalytic processes. Nonetheless, studies of HEA-based nitrate reduction to ammonia are still at the early stage, and it remains unclear how the HEA compositions affect the adsorption and activation of the reaction intermediates. Herein, high-throughput density functional theory (DFT) calculations were integrated with machine learning to investigate the dependence of nitrate adsorption on the FeCoNiCuZn HEA structures. In particular, a total of 1268 different structures were sampled and constructed from the multidimensional configuration space, followed by the DFT calculations to investigate the Gibbs free energy of nitrate adsorption (i.e., ΔGNO3) on different surface microstructures. Four regression models were successfully developed, which can accurately predict ΔGNO3 using the HEA structures as the input features. Through the analysis of the feature importance, it was found that the active sites are crucial for nitrate adsorption; meanwhile, the local environments also play a considerable role. The dependence of the ΔGNO3 and adsorption geometries on the HEA compositions demonstrates that the compositional modulation of the HEA catalysts could be a promising avenue for facile adsorption and activation of reaction intermediates. Overall, this work will contribute to the probabilistic optimization of the HEA microstructures for enhanced electrochemical nitrate reduction.

Hot oscillating pressing sintered AlCoCrFeNi/nanodiamond high-entropy alloy composites

AlCoCrFeNi/nanodiamond high-entropy alloy composites were successfully prepared by hot oscillating pressing (HOP) at different sintering temperatures in this work. The microstructures of the HOPed composites consisted of the FCC phase, BCC phase and nanocarbide phase uniformly distributed in the particles interstices. With the temperature increasing, the original powder particle boundaries gradually disappeared, the density progressively increased, and the microstructure defects decreased. At 1100 °C, its maximum density reached to 99.7 %. Moreover, the FCC phase volume fraction and carbide content further increased without significant microstructure coarsening. The hardness and corrosion resistance of the HOPed samples were better than the hot pressing (HP) samples because the original particle boundaries and pores became smaller, and a small number of nanocarbides were uniformly distributed. Then its maximum hardness reached to 566.48 HV1, and the minimum corrosion current density and corrosion rate was 2.916 μm/cm2 and 0.013 mm/year, respectively, which was better than other alloys reported.

A strategy for high-entropy copper alloys composition design assisted by deep learning based on data reconstruction and network structure optimization

Complex mapping relationships between high-entropy alloy compositions and performances struggle to precisely elucidate by traditional machine learning models, hindering the accurate and efficient design of high-performance alloys. In this work, a novel alloy design strategy combined self-decision deep neural network, data reconstruction with network structure optimization was proposed, which effectively enhanced the model's ability to predict the mapping relationships between high-dimensional composition spaces and mechanical properties of alloy. Compared to other machine learning methods, significantly improves model prediction accuracy (hardness model: 97.98%; strength model: 94.40%). Through above model, the mechanical properties of 308 new (CuNiMn)-X high-entropy copper alloys was studied added other elements such as Al, Ti, Cr, and Fe, which designed and produced a novel (CuNiMn)60Al20Cr20 alloy with high hardness of 474 HV, high compressive strength of 2322 MPa, and high fracture strain of 25.20%. The primary factors influencing alloy performance and their respective thresholds were also quantitatively revealed (hardness: electronegativity deviation (0.02, 0.1) and nuclear charge deviation (0, 0.25); compressive strength: mean nuclear charge (25, 30) and mean atomic radius (134 p.m.)). These findings provide a new perspective and approach for the design and mechanism understanding of high-performance alloys with complex compositions.

Tailoring high-entropy alloys via commodity powders for metal injection moulding: A feasibility study

High entropy alloys (HEAs) represent a novel frontier in metallurgical advancements, offering exceptional mechanical properties owing to their unique multicomponent nature. This study explores a novel strategy utilising commodity powders - Ni625, Invar36, and CoCrF75 - to tailor HEAs via metal injection moulding (MIM). The objective is to achieve cost-effective manufacturing while maintaining desired properties. The research involves mixing these powders in a specific proportion, integrating them with a sustainable polyethylene glycol-cellulose acetate butyrate binder system, and characterising the resulting feedstocks for MIM processing. Subsequent debinding and sintering steps were executed to densify and form a single face-centered cubic (FCC) phase HEA, followed by comprehensive analyses to evaluate the suitability of the developed HEA compositions. In addition, all MIM stages were thoroughly characterised to control the porosity of the final parts and to ensure a single FCC solid solution with promising mechanical properties in the developed non-equiatomic CoCrFeNiMox-type HEAs.

Convolutional neural network based methodology for flexible phase prediction of high entropy alloys

ABSTRACT The advent of high entropy alloys has created a design space that is unfeasible to explore solely through experimentation, thus necessitating the use of computational methods, such as artificial neural networks. This study proposes a new architecture utilizing a convolutional layer to extract relevant elemental features from the alloy composition without limiting the model to specific elements by treating the elemental composition of the high entropy alloy in a similar manner to a row of pixels, using relevant elemental properties in lieu of color values. This convolutional model was able to predict the crystal structure of solid solutions in a high entropy alloy composition with an accuracy of 89.3% in a six-way classification and the formation of intermetallics with an accuracy of 91.4% during holdout validation, while being capable of accurately predicting the primary crystal structure of high entropy alloys containing elements the model was not trained on. Significant space still exists for further experimentation and improvement with this methodology, including augmenting the available datasets and implementation of additional convolutional layers. Due to the limited interpretability of the model architecture, care should be taken when inferring trends from the model prior to validation through experimental, or well-established computational methods.