High-throughput design of high-performance lightweight high-entropy alloys

High-entropy alloy nanomaterials for electrocatalysis

High-entropy alloys (HEAs) exhibit a remarkable capacity to modulate geometric and electronic structures for the construction of catalysts with unpredictable and exceptional performance, and have attracted substantial acclaim within the domain of materials science. In this comprehensive review, we present a thorough summary of the synthesis and multiple applications of HEAs in the realm of electrocatalysis. Our review encompasses the diverse synthesis methodologies of HEA nanomaterials and their pivotal roles in amplifying electrocatalytic performance in hydrogen evolution reactions (HERs), oxygen evolution reactions (OERs), oxygen reduction reactions (ORRs), alcohol oxidation reactions (AORs), and CO2 reduction reactions (CO2RRs), and more. Furthermore, we address the intricate challenges and promising avenues that lie ahead in this research area. Reviewing recent breakthroughs, emerging paradigms, and prospects on the horizon, it becomes increasingly evident that HEAs harbor immense potential to reshape the landscape of energy conversion and storage, and emerge as paramount contenders for the development of cutting-edge electrocatalytic materials that hold the key to a sustainable energy future.

Hierarchical Porous Nonprecious High-entropy Alloys for Ultralow Overpotential in Hydrogen Evolution Reaction

Water electrolysis is considered the cleanest method for hydrogen production. However, the widespread popularization of water splitting is limited by the high cost and scarce resources of efficient platinum group metals. Hence, it is imperative to develop an economical and high-performance electrocatalyst to improve the efficiency of hydrogen evolution reaction (HER). In this study, a hierarchical porous sandwich structure is fabricated through dealloying FeCoNiCuAl2Mn high-entropy alloy (HEA). This free-standing electrocatalyst shows outstanding HER performance with a very small overpotential of 9.7 mV at 10 mA cm-2 and a low Tafel slope of 56.9 mV dec-1 in 1 M KOH solution, outperforming commercial Pt/C. Furthermore, this electrocatalytic system recorded excellent reaction stability over 100 h with a constant current density of 100 mA cm-2. The enhanced electrochemical activity in high-entropy alloys results from the cocktail effect, which is detected by density functional theory (DFT) calculation. Additionally, micron- and nano-sized pores formed during etching boost mass transfer, ensuring sustained electrocatalyst performance even at high current densities. This work provides a new insight for development in the commercial electrocatalysts for water splitting.

Controllable synthesis of high-entropy alloys

High-entropy alloys (HEAs) involving more than four elements, as emerging alloys, have brought about a paradigm shift in material design. The unprecedented compositional diversities and structural complexities of HEAs endow multidimensional exploration space and great potential for practical benefits, as well as a formidable challenge for synthesis. To further optimize performance and promote advanced applications, it is essential to synthesize HEAs with desired characteristics to satisfy the requirements in the application scenarios. The properties of HEAs are highly related to their chemical compositions, microstructure, and morphology. In this review, a comprehensive overview of the controllable synthesis of HEAs is provided, ranging from composition design to morphology control, structure construction, and surface/interface engineering. The fundamental parameters and advanced characterization related to HEAs are introduced. We also propose several critical directions for future development. This review can provide insight and an in-depth understanding of HEAs, accelerating the synthesis of the desired HEAs.

First principles-based design of lightweight high entropy alloys

Recently, the design of lightweight high entropy alloys (HEAs) with a mass density lower than 5 g/cm3 has attracted much research interest in structural materials. We applied a first principles-based high-throughput method to design lightweight HEAs in single solid-solution phase. Three lightweight quinary HEA families were studied: AlBeMgTiLi, AlBeMgTiSi and AlBeMgTiCu. By comprehensively exploring their entire compositional spaces, we identified the most promising compositions according to the following design criteria: the highest stability, lowest mass density, largest elastic modulus and specific stiffness, along with highest Pugh's ratio. We found that HEAs with the topmost compositions exhibit a negative formation energy, a low density and high specific Young's modulus, but a low Pugh's ratio. Importantly, we show that the most stable composition, Al0.31Be0.15Mg0.14Ti0.05Si0.35 is energetically more stable than its metallic compounds and it significantly outperforms the current lightweight engineering alloys such as the 7075 Al alloy. These results suggest that the designed lightweight HEAs can be energetically more stable, lighter, and stiffer but slightly less ductile compared to existing Al alloys. Similar conclusions can be also drawn for the AlBeMgTiLi and AlBeMgTiCu. Our design methodology and findings serve as a valuable tool and guidance for the experimental development of lightweight HEAs.

The Microstructures, Mechanical Properties, and Deformation Mechanism of B2-Hardened NbTiAlZr-Based Refractory High-Entropy Alloys

The NbTiAlZrHfTaMoW refractory high-entropy alloy (RHEA) system with the structure of the B2 matrix (antiphase domains) and antiphase domain boundaries was firstly developed. We conducted the mechanical properties of the RHEAs at 298 K, 1023 K, 1123 K, and 1223 K, as well as typical deformation characteristics. The RHEAs with low density (7.41~7.51 g/cm3) have excellent compressive-specific yield strength (σYS/ρ) at 1023 K (~131 MPa·cm3/g) and 1123 K (~104.2 MPa·cm3/g), respectively, which are far superior to most typical RHEAs. And, they still keep appropriate plastic deformability at room temperature (ε > 0.35). The superior specific yield strengths are mainly attributed to the solid solution strengthening induced by the Zr element. The formation of the dislocation slip bands with [111](101_) and [111](112_) directions and their interaction provide considerable plastic deformation capability. Meanwhile, dynamic recrystallization and dislocation annihilation accelerate the continuous softening after yielding at 1123 K.

Metal Alloys-Structured Electrocatalysts: Metal-Metal Interactions, Coordination Microenvironments, and Structural Property-Reactivity Relationships

Metal alloys-structured electrocatalysts (MAECs) have made essential contributions to accelerating the practical applications of electrocatalytic devices in renewable energy systems. However, due to the complex atomic structures, varied electronic states, and abundant supports, precisely decoding the metal-metal interactions and structure-activity relationships of MAECs still confronts great challenges, which is critical to direct the future engineering and optimization of MAECs. Here, this timely review comprehensively summarizes the latest advances in creating the MAECs, including the metal-metal interactions, coordination microenvironments, and structure-activity relationships. First, the fundamental classification, design, characterization, and structural reconstruction of MAECs are outlined. Then, the electrocatalytic merits and modulation strategies of recent breakthroughs for noble and non-noble metal-structured MAECs are thoroughly discussed, such as solid solution alloys, intermetallic alloys, and single-atom alloys. Particularly, unique insights into the bond interactions, theoretical understanding, and operando techniques for mechanism disclosure are given. Thereafter, the current states of diverse MAECs with a unique focus on structural property-reactivity relationships, reaction pathways, and performance comparisons are discussed. Finally, the future challenges and perspectives for MAECs are systematically discussed. It is believed that this comprehensive review can offer a substantial impact on stimulating the widespread utilization of metal alloys-structured materials in electrocatalysis.

Accelerated Design for High-Entropy Alloys Based on Machine Learning and Multiobjective Optimization

High-entropy alloys (HEAs) with high hardness and high ductility can be considered as candidates for wear-resistant applications. However, designing novel HEAs with multiple desired properties using traditional alloy design methods remains challenging due to the enormous composition space. In this work, we proposed a machine-learning-based framework to design HEAs with high Vickers hardness (H) and high compressive fracture strain (D). Initially, we constructed data sets containing 172,467 data with 161 features for D and H, respectively. Four-step feature selection was performed, with the selection of 12 and 8 features for the D and H prediction models based on the optimal algorithms of the support vector machine (SVR) and light gradient boosting machine (LightGBM), respectively. The R2 of the well-trained models reached 0.76 and 0.90 for the 10-fold cross validation. Nondominated sorting genetic algorithm version II (NSGA-II) and virtual screening were employed to search for the optimal alloying compositions, and four recommended candidates were synthesized to validate our methods. Notably, the D of three candidates have shown significant improvements compared to the samples with similar H in the original data sets, with increases of 135.8, 282.4, and 194.1% respectively. Analyzing the candidates, we have recommended suitable atomic percentage ranges for elements such as Al (2-14.8 at %), Nb (4-25 at %), and Mo (3-9.9 at %) in order to design HEAs with high hardness and ductility.

Machine Learning Paves the Way for High Entropy Compounds Exploration: Challenges, Progress, and Outlook

Machine learning (ML) has emerged as a powerful tool in the research field of high entropy compounds (HECs), which have gained worldwide attention due to their vast compositional space and abundant regulatability. However, the complex structure space of HEC poses challenges to traditional experimental and computational approaches, necessitating the adoption of machine learning. Microscopically, machine learning can model the Hamiltonian of the HEC system, enabling atomic-level property investigations, while macroscopically, it can analyze macroscopic material characteristics such as hardness, melting point, and ductility. Various machine learning algorithms, both traditional methods and deep neural networks, can be employed in HEC research. Comprehensive and accurate data collection, feature engineering, and model training and selection through cross-validation are crucial for establishing excellent ML models. ML also holds promise in analyzing phase structures and stability, constructing potentials in simulations, and facilitating the design of functional materials. Although some domains, such as magnetic and device materials, still require further exploration, machine learning's potential in HEC research is substantial. Consequently, machine learning has become an indispensable tool in understanding and exploiting the capabilities of HEC, serving as the foundation for the new paradigm of Artificial-intelligence-assisted material exploration.

Accelerated discovery of high-performance Al-Si-Mg-Sc casting alloys by integrating active learning with high-throughput CALPHAD calculations

Scandium is the best alloying element to improve the mechanical properties of industrial Al-Si-Mg casting alloys. Most literature reports devote to exploring/designing optimal Sc additions in different commercial Al-Si-Mg casting alloys with well-defined compositions. However, no attempt to optimize the contents of Si, Mg, and Sc has been made due to the great challenge of simultaneous screening in high-dimensional composition space with limited experimental data. In this paper, a novel alloy design strategy was proposed and successfully applied to accelerate the discovery of hypoeutectic Al-Si-Mg-Sc casting alloys over high-dimensional composition space. Firstly, high-throughput CALculation of PHAse Diagrams (CALPHAD) solidification simulations of ocean of hypoeutectic Al-Si-Mg-Sc casting alloys over a wide composition range were performed to establish the quantitative relation 'composition-process-microstructure'. Secondly, the relation 'microstructure-mechanical properties' of Al-Si-Mg-Sc hypoeutectic casting alloys was acquired using the active learning technique supported by key experiments designed by CALPHAD and Bayesian optimization samplings. After a benchmark in A356-xSc alloys, such a strategy was utilized to design the high-performance hypoeutectic Al-xSi-yMg alloys with optimal Sc additions that were later experimentally validated. Finally, the present strategy was successfully extended to screen the optimal contents of Si, Mg, and Sc over high-dimensional hypoeutectic Al-xSi-yMg-zSc composition space. It is anticipated that the proposed strategy integrating active learning with high-throughput CALPHAD simulations and key experiments should be generally applicable to the efficient design of high-performance multi-component materials over high-dimensional composition space.

A first-principles-based high fidelity, high throughput approach for the design of high entropy alloys

Here, we present a preselected small set of ordered structures (PSSOS) method, a first principles-based high fidelity (HF), high throughput (HT) approach, for fast screening of the large composition space of high entropy alloys (HEAs) to select the most energetically stable, single-phase HEAs. Taking quinary AlCoCrFeNi HEA as an example system, we performed PSSOS calculations on the formation energies and mass densities of 8801 compositions in both FCC and BCC lattices and selected five most stable FCC and BCC HEAs for detailed analysis. The calculation results from the PSSOS approach were compared with existing experimental and first-principles data, and the good agreement was achieved. We also compared the PSSOS with the special quasi-random structures (SQS) method, and found that with a comparable accuracy, the PSSOS significantly outperforms the SQS in efficiency, making it ideal for HF, HT calculations of HEAs.

Materials Data toward Machine Learning: Advances and Challenges

Machine learning (ML) is believed to have enabled a paradigm shift in materials research, and in practice, ML has demonstrated its power in speeding up the cost-efficient discovery of new materials and autonomizing materials laboratories. In this Perspective, current research progress in materials data which are the backbones of ML are reviewed, focusing on high-throughput data generation, standardized data storage, and data representation. More importantly, the challenging issues in materials data that should be overcome to unlock the full potential of ML in materials research and development, including classic 5V (volume, velocity, variety, veracity, and value) issues, 3M (multicomponent, multiscale, and multistage) challenges, co-mining of experimental and computational data, and materials data toward transferable/explainable ML or causal ML, are discussed.

Recent Progress with BCC-Structured High-Entropy Alloys

High-entropy alloys (HEAs) prefer to form single-phase solid solutions (body-centered cubic (BCC), face-centered cubic (FCC), or hexagonal closed-packed (HCP)) due to their high mixing entropy. In this paper, we systematically review the mechanical behaviors and properties (such as oxidation and corrosion) of BCC-structured HEAs. The mechanical properties at room temperature and high temperatures of samples prepared by different processes (including vacuum arc-melting, powder sintering and additive manufacturing) are compared, and the effect of alloying on the mechanical properties is analyzed. In addition, the effects of HEA preparation and compositional regulation on corrosion resistance, and the application of high-throughput techniques in the field of HEAs, are discussed. To conclude, alloy development for BCC-structured HEAs is summarized.

Microstructures and Properties of the Low-Density Al15Zr40Ti28Nb12M(Cr, Mo, Si)5 High-Entropy Alloys

Low-density materials show promising prospects for industrial application in engineering, and have remained a research hotspot. The ingots of Al15Zr40Ti28Nb12Cr5, Al15Zr40Ti28Nb12Mo5 and Al15Zr40Ti28Nb12Si5 high-entropy alloys were prepared using an arc melting method. With the addition of the Cr, Mo, and Si, the phase structures of these alloys changed to a dual phase. The Cr and Mo promote the formation of the B2 phase, while the Si promotes the formation of a large amount of the silicides. The compression yield strengths of these alloys are ~1.36 GPa, ~1.27 GPa, and ~1.35 GPa, respectively. The addition of Si and Cr significantly reduces the compression ductility, and the Al15Zr40Ti28Nb12SiMo5 high-entropy alloy exhibits excellent comprehensive mechanical properties. This work investigated the influence of Cr, Mo, and Si on the phase structures and properties of the low-density Al-Zr-Ti-Nb high-entropy alloys, providing theoretical and scientific support for the development of advanced low-density alloys.

Nanoprecipitate-Strengthened High-Entropy Alloys

Multicomponent high-entropy alloys (HEAs) can be tuned to a simple phase with some unique alloy characteristics. HEAs with body-centered-cubic (BCC) or hexagonal-close-packed (HCP) structures are proven to possess high strength and hardness but low ductility. The faced-centered-cubic (FCC) HEAs present considerable ductility, excellent corrosion and radiation resistance. However, their strengths are relatively low. Therefore, the strategy of strengthening the ductile FCC matrix phase is usually adopted to design HEAs with excellent performance. Among various strengthening methods, precipitation strengthening plays a dazzling role since the characteristics of multiple principal elements and slow diffusion effect of elements in HEAs provide a chance to form fine and stable nanoscale precipitates, pushing the strengths of the alloys to new high levels. This paper summarizes and review the recent progress in nanoprecipitate-strengthened HEAs and their strengthening mechanisms. The alloy-design strategies and control of the nanoscale precipitates in HEAs are highlighted. The future works on the related aspects are outlined.

Neural network-based order parameter for phase transitions and its applications in high-entropy alloys

Phase transition is one of the most important phenomena in nature and plays a central role in materials design. All phase transitions are characterized by suitable order parameters, including the order-disorder phase transition. However, finding a representative order parameter for complex systems is non-trivial, such as for high-entropy alloys. Given the strength of dimensionality reduction of a variational autoencoder (VAE), we introduce a VAE-based order parameter. We propose that the Manhattan distance in the VAE latent space can serve as a generic order parameter for order-disorder phase transitions. The physical properties of our order parameter are quantitatively interpreted and demonstrated by multiple refractory high-entropy alloys. Using this order parameter, a generally applicable alloy design concept is proposed by mimicking the natural mixing process of elements. Our physically interpretable VAE-based order parameter provides a computational technique for understanding chemical ordering in alloys, which can facilitate the development of rational alloy design strategies.