Complex Multicomponent Alloys Research Articles

Accelerated discovery of multi-property optimized Fe–Cu alloys

The next generation of rotating electrical machines requires materials with an optimized combination of magnetic, electrical and mechanical properties. High-throughput experiments can accelerate the discovery of relevant materials. Past work has focused on complex multicomponent alloys in which it is difficult to pinpoint the effect of each component. On the other hand, in this work, Fe–Cu binary alloys were used as a model materials system to determine the feasibility of novel materials discovery through a rapid determination of multiple properties. The microstructures, magnetic, electrical, and mechanical properties of Fe–Cu alloys were rapidly determined. With increasing Cu content in Fe-xCu from 0 to 40 wt.%, the phases present changed from single phase BCC to a two-phase FCC + BCC microstructure. There was a decline in the saturation magnetization (Ms) from 211.3 to 118 emu/g. The hardness value increased from 166.3 HV to 238.5 HV, the coercivity (Hc) ranged from 14.6 to 45.7 Oe, and the resistivity varied between 27.3 and 43.0 μΩ·cm. The increased content of a Cu-rich phase led to a decrease in Ms and grain size, and higher Hc and hardness. Using an accelerated methodology, a significant variation in material properties was determined. Three compositions, i.e., Fe–3Cu, Fe–4Cu and Fe–10Cu, with a good balance of properties were identified for potential use in energy applications.

Nanozymes for biomedical applications: Multi-metallic systems may improve activity but at the cost of higher toxicity?

Nanozymes are nanomaterials with intrinsic enzyme-like activity with selected advantages over native enzymes such as simple synthesis, controllable activity, high stability, and low cost. These materials have been explored as surrogates to natural enzymes in biosensing, therapeutics, environmental protection, and many other fields. Among different nanozymes classes, metal- and metal oxide-based nanozymes are the most widely studied. In recent years, bi- and tri-metallic nanomaterials have emerged often showing improved nanozyme activity, some of which even possess multifunctional enzyme-like activity. Taking this concept even further, high-entropy nanomaterials, that is, complex multicomponent alloys and ceramics like oxides, may potentially enhance activity even further. However, the addition of various elements to increase catalytic activity may come at the cost of increased toxicity. Since many nanozyme compositions are currently being explored for in vivo biomedical applications, such as cancer therapeutics, toxicity considerations in relation to nanozyme application in biomedicine are of vital importance for translation. This article is categorized under: Therapeutic Approaches and Drug Discovery > Emerging Technologies Toxicology and Regulatory Issues in Nanomedicine > Toxicology of Nanomaterials Diagnostic Tools > Diagnostic Nanodevices.

Towards controllable formation of antiphase boundaries (APBs) in the B2 matrices of the Al-Fe based complex multicomponent alloys

Anti-Phase Boundaries (APBs) formation in B2 (ordered Body Centered Cubic) structures was investigated, focusing on the Al-Fe-Cr/Ni alloys. The current research goal was to propose strategies for controlling APBs formation in multicomponent alloys, following their positive effect on properties of High Entropy Alloys, stated in previous researches. This study is based on our previous investigation of the APBs formation mechanisms in binary B2 Fe50+xAl50-x alloys. There, stable APBs have formed at and above specific Long-Range order parameter. Here, it was established that stabilization of APBs requires disorder at only Al sublattice. Thorough analysis of dislocations revealed that higher complexity accelerates the decrease of order, promoting the formation of APBs. An assessment of disorder in multicomponent systems is challenging and sometimes even impossible, therefore suggested here usage of evaluation of dislocations is an interesting and useful experimental tool to estimate the disorder in these complex systems.

Sigma Phase Stabilization by Nb Doping in a New High-Entropy Alloy in the FeCrMnNiCu System: A Study of Phase Prediction and Nanomechanical Response

The development of high-entropy alloys has been hampered by the challenge of effectively and verifiably predicting phases using predictive methods for functional design. This study validates remarkable phase prediction capability in complex multicomponent alloys by microstructurally predicting two novel high-entropy alloys in the FCC + BCC and FCC + BCC + IM systems using a novel analytical method based on valence electron concentration (VEC). The results are compared with machine learning, CALPHAD, and experimental data. The key findings highlight the high predictive accuracy of the analytical method and its strong correlation with more intricate prediction methods such as random forest machine learning and CALPHAD. Furthermore, the experimental results validate the predictions with a range of techniques, including SEM-BSE, EDS, elemental mapping, XRD, microhardness, and nanohardness measurements. This study reveals that the addition of Nb enhances the formation of the sigma (σ) intermetallic phase, resulting in increased alloy strength, as demonstrated by microhardness and nanohardness measurements. Lastly, the overlapping VEC ranges in high-entropy alloys are identified as potential indicators of phase transitions at elevated temperatures.

A novel method to examine the effect of Al addition on the microstructure of CoCrFeNiMn high-entropy alloy

A novel approach was employed to investigate the effect of Al addition on the microstructure of a CoCrFeNiMn high-entropy alloy (HEA). A two-layer structure consisting of CoCrFeNiMn with a low-melting Al interlayer was subjected to heat treatment at temperatures of 600, 700, and 800 °C for up to 48 h. The purpose was to directly examine phase formation and phase stability in Al-added alloys. As a result, the heating process at 700 and 800 °C led to the formation of a few microns multilayers, primarily composed of B2-type NiAl and σ phases at the interface and the earlier temperature showed relatively good bonding. These experimental findings were complemented by CALPHAD predictions and considered physical aspects for phase prediction. This concept holds significant benefits for alloy design as it offers a cost-effective and time-saving method to investigate phase formation and stability at different temperatures in complex multi-component alloys.

Performance of two complementary machine-learned potentials in modelling chemically complex systems

Chemically complex multicomponent alloys possess exceptional properties derived from an inexhaustible compositional space. The complexity however makes interatomic potential development challenging. We explore two complementary machine-learned potentials—the moment tensor potential (MTP) and the Gaussian moment neural network (GM-NN)—in simultaneously describing configurational and vibrational degrees of freedom in the Ta-V-Cr-W alloy family. Both models are equally accurate with excellent performance evaluated against density-functional-theory. They achieve root-mean-square-errors (RMSEs) in energies of less than a few meV/atom across 0 K ordered and high-temperature disordered configurations included in the training. Even for compositions not in training, relative energy RMSEs at high temperatures are within a few meV/atom. High-temperature molecular dynamics forces have similarly small RMSEs of about 0.15 eV/Å for the disordered quaternary included in, and ternaries not part of training. MTPs achieve faster convergence with training size; GM-NNs are faster in execution. Active learning is partially beneficial and should be complemented with conventional human-based training set generation.

Redesigning OpenKMC for Multi-Component Trillion-Atom Simulations on the New Sunway Supercomputer

The atomic kinetic Monte Carlo method plays an important role in material simulations by connecting the microscale mechanism with macroscale evolution. However, the long-time simulation of multi-component materials is highly challenging because it demands significant computing resources. With the advent of exascale computing, ultra-high computing power can enable kinetic Monte Carlo (KMC) simulations. In this paper, we deeply optimize OpenKMC for the new-generation Sunway supercomputer. This includes optimizing the memory access for the SW39000 architecture, eliminating various redundant computations at growing scales, and proposing a communication strategy for heterogeneous platforms. In addition, we expanded OpenKMC's simulation for multi-component alloys. Finally, the acceleration framework can produces a <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$37\times$</tex-math></inline-formula> performance enhancement on the Sunway platform. Furthermore, when powered by 10 million cores, our program can perform trillion-atom simulations of complex multi-component alloys with 85% parallel efficiency.

Predicting grain boundary energies of complex alloys from ab initio calculations

Investigating the grain boundary energies of pure fcc metals and their surface energies obtained from ab initio modeling, we introduce a robust method to estimate the grain boundary energies for complex multicomponent alloys. The input parameter is the surface energy of the alloy, which can easily be accessed by modern ab initio calculations based on density functional theory. The method is demonstrated in the case of paramagnetic Fe-Cr-Ni alloys for which experimental grain boundary data is available.

A novel 3D cellular automata-phase field model for computationally efficient dendrite evolution during bulk solidification

Large-domain 3D microstructure prediction that balances both efficiency and accuracy remains a challenging task of materials science and engineering communities. This paper presents a novel 3D Cellular Automata-Phase Field (CA-PF) model that can accurately predict the dendrite formation in a large domain, which combines a 3D CA model with a 1D PF component. In this integrated model, the PF component reformulated in a spherical coordinate is employed to accurately calculate the local growth kinetics including the growth velocity and solute partition at the solidification front while the 3D CA component uses the growth kinetics as inputs to update the dendritic morphology variation and composition redistribution throughout the entire domain. Taking advantage of the high efficiency of the CA model and the high fidelity of the PF model, the 3D CA-PF model saves the computational cost more than five orders of magnitude compared to the 3D PF models without losing much accuracy. By coupling the thermodynamic and kinetic calculations into the PF component, the CA-PF model is capable of handling the microstructure evolution of any complex multi-component alloys. A 2D and a 3D phase-field benchmark problems were first adopted to validate the single dendrites predicted by the 3D CA-PF model. Then, the 3D CA-PF model is applied to predicting the dendrite growth during large-scale solidification processes of directional solidification of Al-30wt.%Cu and laser welding of Al-Cu-Mg and Al-Si-Mg alloys.

Fundamental electronic structure and multiatomic bonding in 13 biocompatible high-entropy alloys

High-entropy alloys (HEAs) have attracted great attention due to their many unique properties and potential applications. The nature of interatomic interactions in this unique class of complex multicomponent alloys is not fully developed or understood. We report a theoretical modeling technique to enable in-depth analysis of their electronic structures and interatomic bonding, and predict HEA properties based on the use of the quantum mechanical metrics, the total bond order density (TBOD) and the partial bond order density (PBOD). Application to 13 biocompatible multicomponent HEAs yields many new and insightful results, including the inadequacy of using the valence electron count, quantification of large lattice distortion, validation of mechanical properties with experiment data, modeling porosity to reduce Young’s modulus. This work outlines a road map for the rational design of HEAs for biomedical applications.

Element interactions and local structure in molten NiRe and NiAlRe alloys: Implications for the aggregation and partition of Re

The element interactions and local structure in molten NiRe and NiAlRe alloys have been investigated via ab initio molecular dynamics (AIMD) simulations. It has been found that the self-interaction of Re evolves from repulsion to attraction with increased Re content, i.e., Re experiences a transition from ordering to clustering, and the trend is further enhanced by Al alloying to NiRe alloys. The finding implies that the aggregation of Re, which is believed to be the main mechanism of Re in improving the mechanical properties of Ni-based single crystal superalloys, may be a kind of positive-feedback process. Our simulation plus experimental results also reveal that the addition of Al enlarges the partition coefficient of Re, which obviously will promote the formation of freckle defect. Both chemical short-range order (CSRO) and relative compactness of solute-centered coordination shell compared with solvent-centered one, obtained in AIMD simulations, are successful in judging the variation of the partition coefficient of Re with added Al. The two quantities may be also useful in revealing the partitioning behavior of elements in complex multi-component alloys whose experimental interaction parameters are lacking.

Ab initio vibrational free energies including anharmonicity for multicomponent alloys

The unique and unanticipated properties of multiple principal component alloys have reinvigorated the field of alloy design and drawn strong interest across scientific disciplines. The vast compositional parameter space makes these alloys a unique area of exploration by means of computational design. However, as of now a method to compute efficiently, yet with high accuracy the thermodynamic properties of such alloys has been missing. One of the underlying reasons is the lack of accurate and efficient approaches to compute vibrational free energies—including anharmonicity—for these chemically complex multicomponent alloys. In this work, a density-functional-theory based approach to overcome this issue is developed based on a combination of thermodynamic integration and a machine-learning potential. We demonstrate the performance of the approach by computing the anharmonic free energy of the prototypical five-component VNbMoTaW refractory high entropy alloy.

Oxidation of amorphous alloys

Owing to their unique short- or medium-range ordered microstructures and excellent mechanical, physical, and chemical properties, amorphous alloys have attracted significant interest in recent years. For the application of amorphous alloys, clarifying their oxidation processes and mechanisms is necessary since many of the surface-related properties of amorphous alloys largely depend on the surface oxide layer. The aim of this paper is to review the recent research on the thermal oxidation behaviors of amorphous alloys under pure oxygen or air condition. The contents are divided into three categories according to the number of components the research considers, i.e., the oxidation of binary, ternary, and multi-component (>3) amorphous alloys. Each section discusses the thermal stability of the amorphous matrix, oxidation kinetics, and the oxide layer and amorphous substrate, which are strongly affected by internal factors (i.e., alloy elements and microstructure) and external factors (i.e., oxidation temperature, duration, and oxygen partial pressure, etc.). The general features of the oxidation of amorphous alloys – from simple binary to complex multi-component amorphous alloys – will be summarized. This overview of the current scientific understanding on the fundamentals of these materials may provide guidelines for the development of strongly corrosion-resistant amorphous alloys.

The configurational entropy of mixing of metastable random solid solution in complex multicomponent alloys

Since the advent of “high-entropy” alloys, the simple ideal mixing rule has been commonly used to calculate the configurational entropy of mixing for these multicomponent alloys. However, there have been increasing experimental evidence reported recently showing that the ideal mixing rule tends to overestimate the configurational entropy of mixing in the multicomponent alloys, particularly at a low temperature. In contrast to the ideal mixing rule, here we provide a formula to assess the configurational entropy of mixing in random solid-solution multicomponent alloys by considering the possible correlations among the constituent elements due to various factors, such as atomic size misfit and chemic bond misfit, which may disturb the potential energy of the system and thus reduce the configurational entropy of mixing. With our entropy formulation, the correlation is explored between the configuration entropy of mixing of different alloys and the general character of the phases formed, such as single- or multiple-phased crystalline phase versus amorphous phase. Being in good agreement with the simulation and experimental results, our work provides an analytical framework that could be further used to explore phase stability in complex multicomponent alloys.

A new microsegregation model for rapid solidification multicomponent alloys and its application to single-crystal nickel-base superalloys of laser rapid directional solidification

The microsegregation in single-crystal nickel-base superalloys, which may result in many undesired precipitated phases, can be effectively reduced by rapid solidification. A new microsegregation model for rapid solidification multicomponent alloys was developed in this paper to predict the microsegregation in the single-crystal superalloys of laser rapid directional solidification and study the influence of processing parameters on the microsegregation behavior. To verify the predicted accuracy of the present model, the concentration profiles and microsegregation ranges under different processing conditions were examined and compared with those calculated by different models. Compared with previous models, this multicomponent model not only has definite physical meaning but also takes nonequilibrium solidification and dendritic tip undercooling into account. Therefore, it is suitable for a wide range of solidification conditions and can obtain better predicted results for complex multicomponent alloys. Moreover, although the microsegregation behavior depends primarily on the solidification velocity, a high temperature gradient also contributes to reducing the microsegregation. Consequently, the microsegregation can be effectively controlled by using a set of processing parameters that can obtain high temperature gradient and solidification velocity. Such a model contributes to the control of the microsegregation while determining relevant processing windows for the preparation of superalloy components using a controlled single-crystal laser processing.

From Electronic Structure to Thermodynamics of Actinide-Based Alloys

In this brief review, we show that thermodynamic modeling of complex multicomponent actinide-based alloys is crucial for fuel development and for predicting the impact of evolving fuel chemistry with time on materials performance. With input from energetics and equilibrium properties of alloys from ab initio electronic-structure calculations, within the framework of density-functional theory, the CALPHAD methodology is a viable approach to thermodynamic assessment for this class of materials. Despite the limited availability of experimental thermodynamic data, this approach can predict important features in the phase diagram and, perhaps more importantly, guide and motivate further experiments for validating the methodology and the data for subsequent modeling of materials performance on a higher level.

Estimation of the possibility of the evaporative refining of nickel alloys from detrimental impurities in vacuum

An expression for the indicator-determinant that allows one to estimate the possibility of removal of a certain component of an alloy due to its preferred evaporation when the metal is held in vacuum is obtained for complex multicomponent alloys. The possibility of vacuum evaporative refining of pure nickel and complex nickel superalloys from detrimental impurities is estimated. The influence of the alloying components of these alloys on the evaporation of detrimental impurities is shown.

Atomistic Modeling of Multicomponent Systems

The need to accelerate materials design programs based on economical and efficient modeling techniques provides the framework for the introduction of approximations in otherwise rigorous theoretical schemes. Several quantum approximate methods have been introduced through the years, bringing new opportunities for the efficient understanding of complex multicomponent alloys at the atomic level. As a promising example of the role that these methods might have in the development of complex systems, in this work we discuss the Bozzolo-Ferrante-Smith (BFS) method for alloys and its application to a variety of multicomponent systems for a detailed analysis of their defect and phase structure and their properties. Examples include the study of the phase structure of new Ru-rich Ni-base superalloys, the role of multiple alloying additions in high temperature intermetallic alloys, and interfacial phenomena in nuclear materials, highlighting the benefits that can be obtained from introducing simple modeling techniques to the investigation of complex systems.

Confirming Computer Calculations of Phase Stability with the Experimental Observations in Automotive Alloy AA6111

AA6111 sheet alloy has been used in automotive panel applications in North America and Europe for several years. This alloy exhibits an excellent combination of strength, formability, ageing response and surface appearance following forming and painting operations. Such a combination of properties is obtained by carefully tailoring the processing route to obtain the desired microstructure of the alloy. In recent years, the ability to predict the phase stability in different alloys has improved significantly, and it is now relatively easy to predict the particles that could form in complex multi component alloys during different processing steps. The accuracy of the predictions is dependent on whether or not the free energy expressions used in the calculations are correct. In this study, the AA6111 alloy was subjected to various annealing treatments that are reflective of different phase fields computed by the Thermo-Calc software. The particles were extracted using the phenol extraction technique and were identified using energy dispersive analysis. The interrelation of the particle analyses with the computed phase stability in AA6111 is presented.

Interface between quantum-mechanical-based approaches, experiments, and CALPHAD methodology

The increased application of quantum-mechanical-based methodologies to the study of alloy stability has required a re-assessment of the field. The focus is mainly on inorganic materials in the solid state. In a first part, after a brief overview of the so-called ab initio methods with their approximations, constraints, and limitations, recommendations are made for a good usage of first-principles codes with a set of qualifiers. Examples are given to illustrate the power and the limitations of ab initio codes. However, despite the “success” of these methodologies, thermodynamics of complex multi-component alloys, as used in engineering applications, requires a more versatile approach presently afforded within CALPHAD. Hence, in a second part, the links that presently exist between ab initio methodologies, experiments, and the CALPHAD approach are examined with illustrations. Finally, the issues of dynamical instability and of the role of lattice vibrations that still constitute the subject of ample discussions within the CALPHAD community are revisited in the light of our current knowledge with a set of recommendations.