Predictive multiphase evolution in Al-containing high-entropy alloys

Additively manufactured high-entropy alloys for hydrogen storage: Predictions

This review paper covers an analysis of the empirical calculations, additive manufacturing (AM) and hydrogen storage of refractory high-entropy alloys undertaken to determine the structural compositions, particularly focusing on their applicability in research and experimental settings. The inventors of multi-component high-entropy alloys (HEAs) calculated that trillions of materials could be manufactured from elements in the periodic table, estimating a vast number, N = 10^100, using Stirling's approximation. The significant contribution of semi-empirical parameters such as Gibbs free energy ΔG, enthalpy of mixing ΔH mix , entropy of mixing ΔS mix , atomic size difference Δδ, valence electron concentration VEC, and electronegativity difference Δχ are to predict BCC and/or FCC phases in HEAs. Additive manufacturing facilitates the determination of refractory HEAs systems with the most stable solid-solution and single-phase, and their subsequent hydrogen storage capabilities. Hydride materials, especially those from HEAs manufactured by AM as bulk and solid materials, have great potential for H2 storage, with storage capacities that can be as high as 1.81 wt% of H2 adsorbed for a ZrTiVCrFeNi system. Furthermore, laser metal deposition (LMD) is the most commonly employed technique for fabricating refractory high entropy alloys, surpassing other methods in usage, thus making it particularly suitable for H2 storage.

The Effects of Heat Treatment on the Microstructure and Mechanical Properties of a Selective Laser Melted AlCoFeNi Medium-Entropy Alloy

A single body-centered cubic (BCC)-structured AlCoFeNi medium-entropy alloy (MEA) was prepared by the selective laser melting (SLM) technique. The hardness of the as-built sample was around 32.5 HRC. The ultimate tensile strength (UTS) was around 1211 MPa, the yield strength (YS) was around 1023 MPa, and the elongation (El) was around 10.8%. A novel BCC + B2 + face-centered cubic (FCC) structure was formed after aging. With an increase in aging temperature and duration, the number of fine grains increased, and more precipitates were observed. After aging at 450 °C for 4 h, the formed complex polyphase structure significantly improved the mechanical properties. Its hardness, UTS, YS, and El were around 45.7 HRC, 1535 MPa, 1489 MPa, and 8.5%, respectively. The improvement in mechanical properties was mainly due to Hall-Petch strengthening, which was caused by fine grains, and precipitation strengthening, which was caused by an increase in precipitates after aging. Meanwhile, the FCC precipitates made the alloy have good toughness. The complex interaction of multiple strengthening mechanisms leads to a good combination of strength, hardness, and toughness.

Combination of Rapid Intrinsic Activity Measurements and Machine Learning as a Screening Approach for Multicomponent Electrocatalysts

Machine learning (ML) coupled with quantum chemistry calculations predicts catalyst properties with high accuracy; however, ML approaches in the design of multicomponent catalysts primarily rely on simulation data because obtaining sufficient experimental data in a short time is difficult. Herein, we developed a rapid screening strategy involving nanodroplet-mediated electrodeposition using a carbon nanocorn electrode as the support substrate that enables complete data collection for training artificial intelligence networks in one week. The inert support substrate ensures intrinsic activity measurement and operando characterization of the irreversible reconstruction of multinary alloy particles during the oxygen evolution reaction. Our approach works as a closed loop: catalyst synthesis-in situ measurement and characterization-database construction-ML analysis-catalyst design. Using artificial neural networks, the ML analysis revealed that the entropy values of multicomponent catalysts are proportional to their catalytic activity. The catalytic activities of high-entropy systems with different components varied little, and the overall catalytic activity was greater than that of the medium-low-entropy system. These findings will serve as a guideline for the design of catalysts.

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.

Direct Visualization of the Self-Alignment Process for Nanostructured Block Copolymer Thin Films by Transmission Electron Microscopy

Herein, this work aims to directly visualize the morphological evolution of the controlled self-assembly of star-block polystyrene-block-polydimethylsiloxane (PS-b-PDMS) thin films via in situ transmission electron microscopy (TEM) observations. With an environmental chip, possessing a built-in metal wire-based microheater fabricated by the microelectromechanical system (MEMS) technique, in situ TEM observations can be conducted under low-dose conditions to investigate the development of film-spanning perpendicular cylinders in the block copolymer (BCP) thin films via a self-alignment process. Owing to the free-standing condition, a symmetric condition of the BCP thin films can be formed for thermal annealing under vacuum with neutral air surface, whereas an asymmetric condition can be formed by an air plasma treatment on one side of the thin film that creates an end-capped neutral layer. A systematic comparison of the time-resolved self-alignment process in the symmetric and asymmetric conditions can be carried out, giving comprehensive insights for the self-alignment process via the nucleation and growth mechanism.

Ultrathin Li-rich Li-Cu alloy anode capped with lithiophilic LiC6 headspace enabling stable cyclic performance

Li-rich dual-phase Li-Cu alloy is a promising candidate toward practical application of Li metal anode due to its in situ formed unique three-dimensional (3D) skeleton of electrochemical inert LiCu_x solid-solution phase. Since a thin layer of metallic Li phase appears on the surface of as-prepared Li-Cu alloy, the LiCu_x framework cannot regulate Li deposition efficiently in the first Li plating process. Herein, a lithiophilic LiC₆ headspace is capped on the upper surface of the Li-Cu alloy, which can not only offer free space to accommodate Li deposition and maintain dimensional stability of the anode, but also provide abundant lithiophilic sites and guide Li deposition effectively. This unique bilayer architecture is fabricated via a facile thermal infiltration method, where the Li-Cu alloy layer with an ultrathin thickness around 40 μm occupies the bottom of a carbon paper (CP) sheet, and the upper part of this 3D porous framework is reserved as the headspace for Li storage. Notably, the molten Li can quickly convert these carbon fibers of the CP into lithiophilic LiC₆ fibers while the CP is touched with the liquid Li. The synergetic effect between the LiC₆ fibers framework and LiCu_x nanowires scaffold can ensure a uniform local electric field and stable Li metal deposition during cycling. As a consequence, the CP capped ultrathin Li-Cu alloy anode demonstrates excellent cycling stability and rate capability.

Performance-oriented multistage design for multi-principal element alloys with low cost yet high efficiency

Multi-principal element alloys (MPEAs) with remarkable performances possess great potential as structural, functional, and smart materials. However, their efficient performance-orientated design in a wide range of compositions and types is an extremely challenging issue, because of properties strongly dependent upon the composition and composition-dominated microstructure. Here, we propose a multistage-design approach integrating machine learning, physical laws and a mathematical model for developing the desired-property MPEAs in a very time-efficient way. Compared to the existing physical model- or machine-learning-assisted material development, the forward-and-inverse problems, including identifying the target property and unearthing the optimal composition, can be tackled with better efficiency and higher accuracy using our proposed avenue, which defeats the one-step component-performance design strategy by multistage-design coupling constraints. Furthermore, we developed a new multi-phase MPEA at the minimal time and cost, whose high strength-ductility synergy exceeded those of its system and subsystem reported so far by searching for the optimal combination of phase fraction and composition. The present work suggests that the property-guided composition and microstructure are precisely tailored through the newly built approach with significant reductions of the development period and cost, which is readily extendable to other multi-principal element materials.

Highly complex magnetic behavior resulting from hierarchical phase separation in AlCo(Cr)FeNi high-entropy alloys

Magnetic high-entropy alloys (HEAs) are a new category of high-performance magnetic materials, with multicomponent concentrated compositions and complex multi-phase structures. Although there have been numerous reports of their interesting magnetic properties, there is very limited understanding about the interplay between their hierarchical multi-phase structures and the resulting magnetic behavior. We reveal for the first time the influence of a hierarchically decomposed B2 + A2 structure in an AlCo_0.5Cr_0.5FeNi HEA on the formation of magnetic vortex states within individual A2 (disordered BCC) precipitates, which are distributed in an ordered B2 matrix that is weakly ferromagnetic. Non-magnetic or weakly ferromagnetic B2 precipitates in large magnetic domains of the A2 phase, and strongly magnetic Fe-Co-rich interphase A2 regions, are also observed. These results provide important insight into the origin of coercivity in this HEA, which can be attributed to a complex magnetization process that includes the successive reversal of magnetic vortices.

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Al(Co,Cr)FeNi alloys consist of hierarchically decomposed B2 + magnetic A2 phases

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In AlCoFeNi, nanosized phases form magnetic domains with small angle alignment

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In AlCo_0.5Cr_0.5FeNi, B2 region contains A2 magnetic vortices and A2 with B2 inclusions

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The switching behavior of the magnetic microstructure is related to soft magnetism

Gradient cell-structured high-entropy alloy with exceptional strength and ductility

[Figure: see text].

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.

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

Developing affordable and light high-temperature materials alternative to Ni-base superalloys has significantly increased the efforts in designing advanced ferritic superalloys. However, currently developed ferritic superalloys still exhibit low high-temperature strengths, which limits their usage. Here we use a CALPHAD-based high-throughput computational method to design light, strong, and low-cost high-entropy alloys for elevated-temperature applications. Through the high-throughput screening, precipitation-strengthened lightweight high-entropy alloys are discovered from thousands of initial compositions, which exhibit enhanced strengths compared to other counterparts at room and elevated temperatures. The experimental and theoretical understanding of both successful and failed cases in their strengthening mechanisms and order-disorder transitions further improves the accuracy of the thermodynamic database of the discovered alloy system. This study shows that integrating high-throughput screening, multiscale modeling, and experimental validation proves to be efficient and useful in accelerating the discovery of advanced precipitation-strengthened structural materials tuned by the high-entropy alloy concept.

Advanced screening strategies for the design of high-entropy alloys are highly desirable. Here the authors use the project-oriented design strategy and CALPHAD-based high-throughput calculation tool to rapidly screen promising Al-Cr-Fe-Mn-Ti structural HEAs for high-temperature applications.

High-entropy materials for catalysis: A new frontier

Entropy plays a pivotal role in catalysis, and extensive research efforts have been directed to understanding the enthalpy-entropy relationship that defines the reaction pathways of molecular species. On the other side, surface of the catalysts, entropic effects have been rarely investigated because of the difficulty in deciphering the increased complexities in multicomponent systems. Recent advances in high-entropy materials (HEMs) have triggered broad interests in exploring entropy-stabilized systems for catalysis, where the enhanced configurational entropy affords a virtually unlimited scope for tailoring the structures and properties of HEMs. In this review, we summarize recent progress in the discovery and design of HEMs for catalysis. The correlation between compositional and structural engineering and optimization of the catalytic behaviors is highlighted for high-entropy alloys, oxides, and beyond. Tuning composition and configuration of HEMs introduces untapped opportunities for accessing better catalysts and resolving issues that are considered challenging in conventional, simple systems.

Digital Transformation in Materials Science: A Paradigm Change in Material's Development

The ongoing digitalization is rapidly changing and will further revolutionize all parts of life. This statement is currently omnipresent in the media as well as in the scientific community; however, the exact consequences of the proceeding digitalization for the field of materials science in general and the way research will be performed in the future are still unclear. There are first promising examples featuring the potential to change discovery and development approaches toward new materials. Nevertheless, a wide range of open questions have to be solved in order to enable the so-called digital-supported material research. The current state-of-the-art, the present and future challenges, as well as the resulting perspectives for materials science are described.

Role of Static Displacements in Stabilizing Body Centered Cubic High Entropy Alloys

The configurational entropy of high entropy alloys (HEAs) plays little role in the stabilization of one particular crystal structure over another. We show that disorder-induced atomic displacements help stabilize body centered cubic (bcc) structure HEAs with average valences <4.7. These disorder-induced atomic displacements mimic the temperature-induced vibrations that stabilize the bcc structure of group IV elemental metals at high temperatures. The static displacements are significantly larger than for face centered cubic HEAs, approaching values associated with the Lindemann criterion for melting. Chemical disorder in high entropy alloys have a previously unidentified, nonentropic energy contribution that stabilizes a particular crystalline ground state.

Low-temperature fusion fabrication of Li-Cu alloy anode with in situ formed 3D framework of inert LiCux nanowires for excellent Li storage performance

The commercialization of rechargeable Li metal batteries is hindered by dendrite growth and volumetric variation. Herein, we report a Li-rich dual-phase Li-Cu alloy with built-in 3D conductive skeleton to replace conventional planar Li anode. The Li-Cu alloy is simply prepared by fusion of Li and Cu metals at a relatively low-temperature of 500 °C, followed by a cooling process where phase-segregation leads to metallic Li phase distributed in the network of LiCu_x solid solution phase. Different from the common Li alloy, the electrochemical alloying reaction between Li and Cu metals is not observed. Therefore, the lithiophilic LiCu_x nanowires guides conformal plating of Li and the porous framework provides superior dimensional stability for the anode. This unique ferroconcrete-like structure of Li-Cu alloy enables dendrite-free Li plating for an expanded cycling lifetime. Constructing a new type of Li alloy with in situ formed electrochemically inactive framework is a promising and easily scaled-up strategy toward practical application of Li metal anodes.

Irradiation-Induced Extremes Create Hierarchical Face-/Body-Centered-Cubic Phases in Nanostructured High Entropy Alloys

A nanoscale hierarchical dual-phase structure is reported to form in a nanocrystalline NiFeCoCrCu high-entropy-alloy (HEA) film via ion irradiation. Under the extreme energy deposition and consequent thermal energy dissipation induced by energetic particles, a fundamentally new phenomenon is revealed, in which the original single-phase face-centered-cubic (FCC) structure partially transforms into alternating nanometer layers of a body-centered-cubic (BCC) structure. The orientation relationship follows the Nishiyama-Wasser-man relationship, that is, (011)_BCC || ( 1¯1¯1)_FCC and [100]_BCC || [ 11¯0]_FCC . Simulation results indicate that Cr, as a BCC stabilizing element, exhibits a tendency to segregate to the stacking faults (SFs). Furthermore, the high densities of SFs and twin boundaries in each nanocrystalline grain serve to accelerate the nucleation and growth of the BCC phase during irradiation. By adjusting the irradiation parameters, desired thicknesses of the FCC and BCC phases in the laminates can be achieved. This work demonstrates the controlled formation of an attractive dual-phase nanolaminate structure under ion irradiation and provides a strategy for designing new derivate structures of HEAs.

Mechanism of collective interstitial ordering in Fe-C alloys

Collective interstitial ordering is at the core of martensite formation in Fe-C-based alloys, laying the foundation for high-strength steels. Even though this ordering has been studied extensively for more than a century, some fundamental mechanisms remain elusive. Here, we show the unexpected effects of two correlated phenomena on the ordering mechanism: anharmonicity and segregation. The local anharmonicity in the strain fields induced by interstitials substantially reduces the critical concentration for interstitial ordering, up to a factor of three. Further, the competition between interstitial ordering and segregation results in an effective decrease of interstitial segregation into extended defects for high interstitial concentrations. The mechanism and corresponding impact on interstitial ordering identified here enrich the theory of phase transitions in materials and constitute a crucial step in the design of ultra-high-performance alloys.

Tailoring heterogeneities in high-entropy alloys to promote strength-ductility synergy

Conventional alloys are usually based on a single host metal. Recent high-entropy alloys (HEAs), in contrast, employ multiple principal elements. The strength of HEAs is considerably higher than traditional solid solutions, as the many constituents lead to a rugged energy landscape that increases the resistance to dislocation motion, which can also be retarded by other heterogeneities. The wide variety of nanostructured heterogeneities in HEAs, including those generated on the fly during tensile straining, also offer elevated strain-hardening capability that promotes uniform tensile ductility. Citing recent examples, this review explores the multiple levels of heterogeneities in multi-principal-element alloys that contribute to lattice friction and back stress hardening, as a general strategy towards strength-ductility synergy beyond current benchmark ranges.

High Entropy Alloys Mined From Binary Phase Diagrams

High entropy alloys (HEA) are a new type of high-performance structural material. Their vast degrees of compositional freedom provide for extensive opportunities to design alloys with tailored properties. However, compositional complexities present challenges for alloy design. Current approaches have shown limited reliability in accounting for the compositional regions of single solid solution and composite phases. For the first time, a phenomenological method analysing binary phase diagrams to predict HEA phases is presented. The hypothesis is that the HEA structural stability is encoded within the phase diagrams. Accordingly, we introduce several phase-diagram inspired parameters and employ machine learning (ML) to classify 600+ reported HEAs based on these parameters. Compared to other large database statistical prediction models, this model gives more detailed and accurate phase predictions. Both the overall HEA prediction and specifically single-phase HEA prediction rate are above 80%. To validate our method, we demonstrated its capability in predicting HEA solid solution phases with or without intermetallics in 42 randomly selected complex compositions, with a success rate of 81%. The presented search approach with high predictive capability can be exploited to interact with and complement other computation-intense methods such as CALPHAD in providing an accelerated and precise HEA design.