Composition Space Research Articles

Exploration of Chemical Space through Automated Reasoning

The vast size of composition space poses a significant challenge for materials chemistry: exhaustive enumeration of potentially interesting compositions is typically infeasible, hindering assessment of important criteria ranging from novelty and stability to cost and performance. We report a tool, Comgen, for the efficient exploration of composition space, which makes use of logical methods from computer science used for proving theorems. We demonstrate how these techniques, which have not previously been applied to materials discovery, can enable reasoning about scientific domain knowledge provided by human experts. Comgen accepts a variety of user‐specified criteria, converts these into an abstract form, and utilises a powerful automated reasoning algorithm to identify compositions that satisfy these user requirements, or prove that the requirements cannot be simultaneously satisfied. In contrast to machine learning techniques, explicitly reasoning about domain knowledge, rather than making inferences from data, ensures that Comgen’s outputs are fully interpretable and provably correct. Users interact with Comgen through a high‐level Python interface. We illustrate use of the tool with several case studies focused on the search for new ionic conductors. Further, we demonstrate the integration of Comgen into an end‐to‐end automated workflow to propose and evaluate candidate compositions quantitatively, prior to experimental investigation.

Mechanisms of Reservoir Space Preservation in Ultra-Deep Shales: Insights from the Ordovician–Silurian Wufeng–Longmaxi Formation, Eastern Sichuan Basin

This study aims to explore the reservoir characteristics and formation mechanisms of ultra-deep shale gas in the Ordovician–Silurian Wufeng–Longmaxi Formation in the Sichuan Basin in order to provide theoretical support and practical guidance for the exploration and development of ultra-deep shale gas. With recent breakthroughs in ultra-deep shale gas exploration, understanding its organic matter development, mineral composition, and reservoir space characteristics has become particularly important. The background of this research lies in the significant potential of ultra-deep shale gas, which remains inadequately understood, necessitating an in-depth analysis of its pore structure and reservoir quality. Through a systematic study of the ultra-deep shale in well FS1 of Sichuan Basin, that the following was found: (i) The ultra-deep shale in the Wufeng–Longmaxi Formation is mainly composed of quartz and clay minerals, exhibiting high total organic carbon (TOC) and high porosity characteristics, indicating it is in an overmature thermal evolution stage. (ii) Organic pores and microcracks in the ultra-deep shale are more developed compared to middle-shallow and deep shale, forming a complex pore structure that is conducive to gas storage. (iii) In the diagenesis process, the dissolution and recrystallization of the biogenic skeleton promote the cementation between autogenetic quartz particles, forming a rigid skeleton that effectively inhibits the impact of mechanical compaction. (iv) The overpressure environment created by the hydrocarbon generation process, along with gas production from hydrocarbon cracking, can effectively offset the mechanical compaction of overburden pressure on micropores, and this overpressure environment also promotes the further development of microfractures, which is beneficial for the development and preservation of ultra-deep shale pores. In summary, this study not only reveals the reservoir characteristics and formation mechanisms of ultra-deep shale but also provides essential references for the exploration and development of ultra-deep shale gas in the Sichuan Basin and similar regions, emphasizing the ongoing significance of research in this field.

Active Learning Guided Discovery of High Entropy Oxides Featuring High H2-production.

High entropy oxides (HEOs) represent a class of solid solutions comprising multiple elements, offering significant scientific potential. Due to the enormous combination types of elements, the design of HEOs with desirable properties within high-dimensional composition spaces has traditionally relied heavily on knowledge and intuition. In this study, we present an active learning (AL) strategy tailored to efficiently explore the vast compositional space of HEOs. Our approach operates as a closed-loop system, iteratively cycling through "Training, Prediction, and Experiment" stages. Across multiple AL iterations, we have successfully identified four novel HEOs from a vast array of potential compositions. These newly discovered materials exhibit exceptional stability and demonstrate outstanding performance in H2 evolution rate (251 μmol gcat-1 min-1) during the water-gas shift reaction, surpassing benchmarks set by established catalysts such as Pt/γ-Al2O3 (135 μmol gcat-1 min-1) and Cu/ZnO/Al2O3 (81 μmol gcat-1 min-1). X-ray photoelectron spectroscopy and density functional theory calculations revealed a loss of elemental identity in the selected HEOs. This catalyst discovery process underscores the efficacy of Machine Learning in accelerating the identification of HEOs with unique characteristics by effectively leveraging insights from limited experimental data.

Development and Prospect of Space Debris Protection Structures for Spacecraft

Background: As the pace of human conquest of space continues to accelerate, the number of spacecraft carrying out various activities in space continues to increase, so that the available orbital space continues to decrease, the amount of space debris continues to increase, and thus the probability of orbiting spacecraft being impacted by space debris is also increasing. Research on the development of protective structures in the current state is conducive to improving the protection performance of spacecraft protective structures against space debris, reducing the occurrence of spacecraft disintegration events and spacecraft collision events, and may also promote the development of many fields through the development of new technologies. Aim: Through the latest application and development of spacecraft, the advantages and disadvantages of various protection structures are summarized, and the development trend of academic and aerospace protection engineering is analyzed. Methods: Through the latest representative patent research methods for spacecraft space debris, research content, and creative structure, the principle and characteristics of the protective structure are demonstrated. Results: By comparing the application of different spacecraft space debris protection structures, the existing problems of the current protection structures are listed, and the potential development paths and research topics are put forward Conclusion: The development of aerospace, military, and other industries benefit from the development of protective structures, and the composite spacecraft space debris protection structures have broad development prospects.

Towards accelerating discovery via physics-driven and interactive multi-fidelity Bayesian Optimization

Abstract Both computational and experimental material discovery bring forth the challenge of exploring multidimensional and often non-differentiable parameter spaces, such as phase diagrams of Hamiltonians with multiple interactions, composition spaces of combinatorial libraries, processing spaces, and molecular embedding spaces. Often these systems are expensive or time-consuming to evaluate a single instance, and hence classical approaches based on exhaustive grid or random search are too data intensive. This resulted in strong interest towards active learning methods such as Bayesian optimization (BO) where the adaptive exploration occurs based on human learning (discovery) objective. However, classical BO is based on a predefined optimization target, and policies balancing exploration and exploitation are purely data driven. In practical settings, the domain expert can pose prior knowledge on the system in form of partially known physics laws and often varies exploration policies during the experiment. Here, we propose an interactive workflow building on multi-fidelity BO (MFBO), starting with classical (data-driven) MFBO, then expand to a proposed structured (physics-driven) sMFBO, and finally extending it to allow human in the loop interactive iMFBO workflows for adaptive and domain expert aligned exploration. These approaches are demonstrated over highly non-smooth multi-fidelity simulation data generated from an Ising model, considering spin-spin interaction as parameter space, lattice sizes as fidelity spaces, and the objective as maximizing heat capacity. Detailed analysis and comparison show the impact of physics knowledge injection and real time human decisions for improved exploration with increased alignment to ground truth.

Compositional optimization for enhanced oxidation resistance of high-entropy carbide ceramics

Excellent oxidation resistance is essential for the viability of high-entropy carbides (HECs) in high-temperature environments. However, the vast HEC compositional space presents a significant challenge in developing desirable formulations with improved oxidation tolerance. In this work, we establish a computational framework to guide the optimization of oxidation-tolerant HEC compositions and further validate the theoretical predictions through systematic oxidation experiments. Leveraging first-principles calculations, we initially determine the heterogeneous oxygen adsorption ability of different constituent elements in HECs, which is subsequently harnessed to regulate preferential oxidation and suppress the formation of detrimental oxides during the oxidation process. Incorporating this fundamental-level understanding, multi-objective optimization (MOO) was performed to identify a compositional region with potentially intrinsic oxidation immunity, achieving a balance between the compactness of the oxidation product and the thermodynamic stability of the HEC matrix. Based on our analysis, we propose a compositional design strategy involving the strategic reduction of elements with higher oxygen adsorption energies, such as Nb, Ta, and Ti in (NbTaZrHfTi)C, to effectively improve the oxidation performance of HECs. The theoretical findings are robustly corroborated by our comparative oxidation experiments, which demonstrate that the selected NbTa-depletion HEC exhibits superior antioxidant performance compared to the equiatomic counterpart. The integrated computational-experimental approach presented in this work serves as a powerful framework to accelerate the discovery and development of oxidation-resistant HECs, paving the way for their expanded applications in demanding high-temperature, oxygen-rich environments.

Nanostructure and dislocation interactions in refractory complex concentrated alloy: From chemical short-range order to nanoscale B2 precipitates

Refractory complex concentrated alloys (RCCAs) have emerged as a promising class of structural materials, demonstrating exceptional mechanical performance in aggressive environments. However, the complex atomic environments, significant lattice distortion, and vast compositional space of RCCAs present challenges to understanding the mechanisms that govern structure-property relationships. In this study, we explore the dislocation mechanisms in three model quaternary RCCAs, namely Mo25Nb10Ta25W40 (at. %), Mo25Nb25Ta25W25, and Mo25Nb40Ta25W10 using large-scale atomistic simulations and machine learning based Spectral Neighbor Analysis Potential. Our atomistic simulations examine how the chemical composition and local ordering influence the mobility of both edge and screw dislocations, and how lattice distortion and diffuse anti-phase boundary energy (DAPBE) affect dislocation behaviors during nanostructural evolution. Notably, with the increase in Nb concentration in the model RCCAs, both DAPBE and lattice distortion are simultaneously enhanced as the chemical short-range order (CSRO) evolves into nanoscale B2 precipitates. This evolution results in high lattice distortion due to the lattice mismatch between B2 precipitates and the random matrix. Consequently, B2 nanoprecipitates provide a stronger pinning effect, hindering edge dislocation motion while promoting cross-slip of screw dislocations, leading to a reduced screw-to-edge ratio in slip resistance and mobility discrepancy. These findings offer valuable insights into dislocation behaviors and interactions with ordered precipitates, highlighting the importance of exploring non-equiatomic compositions and advancing beyond CSRO in RCCAs. This study has implications for optimizing alloy compositions and processing methods for superior performance in aggressive environments.

Dense nine elemental high entropy diboride ceramics with unique high temperature mechanical and physical properties

Due to their huge composition space and superior performance characteristics, high entropy borides have garnered great interest in many fields, particularly where their high-temperature performances at high temperatures are critical. Herein, we first discovered that dense (Ti1/9Zr1/9Hf1/9Nb1/9Ta1/9V1/9Cr1/9Mo1/9W1/9)B2 (HEB9) ceramics prepared at 1850 °C showed an exceptionally low temperature coefficient of electrical resistivity (4.28 × 10−4 K−1), which is ∼38% of that of (Ti1/5Zr1/5Hf1/5Nb1/5Ta1/5)B2 (HEB5) and nearly an order of magnitude lower than those of previously reported ZrB2 and ZrB2-30 vol% SiC ceramics. Their mechanical, thermophysical properties at elevated temperatures were also investigated systematically. Although the thermal conductivity of HEB9 increased with elevated temperatures, it remained at a very low level (∼29 W/(m·K)) at 1273 K, nearly half that of HEB5. Interestingly, it is found that the thermal conduction of HEB9 and HEB5 was mainly contributed by electrons, suggeting their thermal conductivity could be roughly estimated from corresponding electrical conductivity values. Moreover, HEB9 exhibited a geometrically necessary dislocation density over 30 times greater than HEB5, likely contributing to its higher Vickers hardness and unique physical properties. Notably, the flexural strength of HEB9 at 1600 °C was even improved to 650.0 ± 86.3 MPa, compared to the value (559.7 ± 36.7 MPa) at room temperature without degradation, which was comparable to that of HEB5, although thermodynamic calculations indicated a lower melting point of HEB9 and grain boundary softening was occurred in HEB9 at higher temperatures. The excellent mechanical, electrical and thermophysical properties of HEB9 at elevated temperatures make it a competitive candidate for various high-temperature applications.

A residence time-based concept for modeling and analyzing the impact of diffusion on auto-ignition processes with thermal stratification

A meticulous definition of the residence time (also referred to as the fluid age) in the context of auto-ignition in a thermally stratified gas has demonstrated that the age can be considered as a level-set function, which allows the tracking of a self-ignition front propagating. In order to avoid the difficulty of defining the boundary and initial conditions for this age, which can be seen as fluid birth, a normalized age is introduced. Meticulous derivation of the equation revealed additional terms related to the scalar dissipation rates and the gradient in the composition space of the auto-ignition delay. To validate this model equation, two canonical configurations were proposed: one representing a hot spot self-igniting with significant thermal diffusion and the other where a self-ignition front propagates at an almost constant speed. The analysis, based on the normalized age of the particles, reveals the impact of thermal diffusion on the acceleration or deceleration of particle aging. In certain instances, a particle rejuvenation mechanism through thermal diffusion has been identified. Furthermore, this work demonstrates the capacity of the normalized residence time field to map transitions between auto-ignition and laminar flame. Finally, the different cases studied were classified in an auto-ignition/diffusion diagram based on three non-dimensional characteristic numbers, which highlighted five combustion regimes.

Modeling viscosity of commercial glass melts: Adam–Gibbs equation and Gaussian process regression

ABSTRACT A statistically designed property database of six commercial glass families is leveraged to develop models relating viscosity to temperature and composition. Specialized models for each glass family were designed based on the Adam–Gibbs equation. We show that the whole range of viscosity data of one glass family from 100 to 1012 Pa s can be accurately modeled using one composition-dependent and two composition-independent parameters when glass transition temperature and viscosity at that temperature are independently determined. Generalization to a broader composition space using the same approach leads to a loss of accuracy compared to specialized models. However, this can be mostly negated when Gaussian process regression is used to estimate the compositional dependence of configuration entropy and fragility exponent parameters of the Adam–Gibbs equation.

Superconductivity in Diamond-Like BC15.

Advancing the compositional space of a compound class can result in intriguing superconductors, such as LaH10. Herein, we performed a comprehensive first-principles structural search on a binary B-C system with various chemical compositions. The identified diamond-like BC15, named d-BC15, is thermodynamically superior to the synthesized BC3 and BC5. Interestingly, d-BC15 shows anisotropic superconductivity resulting from three distinct Fermi surfaces. Its predicted critical temperature (Tc) is 43.6 K at ambient pressure, beyond the McMillan limit. d-BC15 reaches a maximum of around 75 K at 0.43% hole doping due to the substantially enhanced density of states at the Fermi level. Additionally, d-BC15 demonstrates superhard characteristics with a Vickers hardness of 75 GPa. The calculated tensile and shear stresses are 72 and 73 GPa, respectively. The combination of high superconductivity and superhardness in d-BC15 offers new insights into the design of multifunctional materials.

New Thermodynamic Models for Anhydrous Alkaline-Silicate Magmatic Systems

Abstract A new thermodynamic model for silicate melt in the Na2O–CaO–K2O–FeO–MgO–Al2O3–SiO2–TiO2–Fe2O3–Cr2O3 model system is presented, building on the tholeiitic through to granitic melt model of Holland et al. (2018) [Journal of Petrology, 59, 881–900] but extending for the first time into anhydrous alkaline systems. The new melt model is accompanied by new thermodynamic models for nepheline, kalsilite, leucite, melilite and ilmenite. Collectively these models enable pseudosection modelling of alkaline-silicate magmatic systems, providing a new tool for investigating this geologically- and economically-important compositional space. The models are calibrated with respect to experimental data on phase relations among minerals and melt, and the fit is benchmarked here via detailed comparison with seven experimental datasets, which encompass a range of pressure (0–22 kbar), temperature (680–1350°C), oxygen fugacity (log fO2 ΔFMQ-3 to +1), total alkali (3–16 wt %) and silica (37–70 wt %) conditions. The calculated pseudosections successfully reproduce experimental crystallisation sequences and phase compositions, indicating that the thermodynamic models are well calibrated across this spectrum of conditions. Redox buffered experimental conditions are simulated using oxygen buffered pseudosections. Contouring of oxygen buffered pseudosections with XFe3+ (mol. Fe3+/Fetotal), or pseudosections of varying XFe3+ with ΔFMQ, reveals (i) often complex and non-intuitive relationships between these two representations of oxidation state, and (ii) substantial variation in ferric iron over narrow temperature intervals in some oxygen buffered sets of experiments. An implication is that simulating oxygen buffering is vital when benchmarking thermodynamic models using experimental results. Furthermore, because natural igneous systems likely feature a near-constant XFe3+, it is important to assess experimental results in this framework when making inferences about natural systems, recognising that oxygen fugacity is a consequence not a control of phase equilibria in nature. Overall, our new models provide a novel tool to explore the role of variables, such as pressure, fractional crystallisation and crustal assimilation in the petrogenesis of alkaline-silicate magmatic systems and their associated mineralisation.

High-throughput exploration of the Na2O-ZrO2-SiO2-P2O5 composition space using thin film material libraries

AbstractThe composition space Na2O-ZrO2-SiO2-P2O5 was explored using thin film material libraries and high-throughput characterization methods. The combinatorial synthesis comprised non-reactive magnetron co-sputtering at room temperature from two elemental targets Zr and Si, and a compound Na3PO4 target followed by a two-step annealing process. The NASICON phase formed for all compositions on the material library produced at the optimized deposition parameters, also displaying a phase-pure Na1+xZr2SixP3−xO12 (x = 0.4—2.9) region. Analytical TEM analysis of the NASICON phase at a measurement area with stoichiometric composition revealed a fine-grained microstructure and homogeneous Si distribution, in contrast to Na-P excess conditions, where large Si-free NASICON grains have formed.

Nano‐High Entropy Materials in Electrocatalysis

AbstractHigh entropy materials (HEMs) compositing of at least five elements have gained widespread attention in the field of electrocatalysis due to their tunable activities and high stability. These intrinsic properties can be further highlighted when the size of HEMs comes to the nanoscale. In nanostructured HEMs, the fascinating properties including large composition space, multi‐element synergy, and high configuration entropy are expected to endow nano‐HEMs with excellent catalytic activity and stability, thus providing greater potential for the design of advanced electrocatalysts. In this review, nano‐HEMs are differentiated in detail in dimensions and the common synthesis methods are summarized. Additionally, from the perspective of the complex nanostructure‐performance relationship, the applications of nano‐HEMs in electrocatalysis systems, including water‐splitting (hydrogen evolution reaction (HER), oxygen evolution reaction (OER)), hydrogen oxidation reaction (HOR), oxygen reduction reaction (ORR), carbon dioxide reduction reaction (CO2RR), nitrogen reduction reaction (NRR) and alcohol oxidation reaction (AOR) are discussed. Finally, the main challenges faced by nano‐HEMs electrocatalysis are underscored. This review is expected to provide more insights into understanding and developing efficient electrocatalytic materials for practical applications.

Cohesive energy discrepancy drives the fabrication of multimetallic atomically dispersed materials for hydrogen evolution reaction

Atomically dispersed single atom (SA) and atomic cluster (AC) metallic materials attract tremendous attentions in various fields. Expanding monometallic SA and AC to multimetallic SA/AC composites opens vast scientific and technological potentials yet exponentially increasing the synthesis difficulty. Here, we present a general energy-selective-clustering methodology to build the largest reported library of carbon supported bi-/multi-metallic SA/AC materials. The discrepancy in cohesive energy results into selective metal clustering thereby driving the symbiosis of multimetallic SA or/and AC. The library includes 23 bimetallic SA/AC composites, and expanded compositional space of 17 trimetallic, quinary-metallic, septenary-metallic SA/AC composites. We chose bimetallic M1SAM2AC to demonstrate the electrocatalysis utility. Unique decoupled active sites and inter-site synergy lead to 8/47 mV overpotential at 10 mA cm−2 for alkaline/acidic hydrogen evolution and over 1000 h durability in water electrolyzer. Moreover, delicate modulations towards composition and configuration yield high-performance catalysts for multiple electrocatalysis systems. Our work broadens the family of atomically dispersed materials from monometallic to multimetallic and provides a platform to explore the complex composition induced unconventional effects.

Phase-field modeling of interdiffusion between dissimilar Fe-Cr-Ni alloys during non-isothermal hot isostatic pressing

Powder metallurgy hot isostatic pressing (PM-HIP) has emerged as a promising alternative to welding for joining dissimilar metals. During HIP, interfacial bonding is mediated by solid state diffusion. The interdiffusion zone across the interface depends on processing conditions, calling for the need for accurate numerical tools capable of simulating interdiffusion and possible phase transformation in order to optimize processing parameters. Here, a phase-field (PF) model based on CALPHAD-based free energy functionals is developed to simulate the interdiffusion and phase evolution between dissimilar Fe–Cr–Ni based steels undergoing HIP and is demonstrated using the interface between 316L and SA508 steels. To overcome the numerical challenges caused by the singular magnetic and entropy terms in the CALPHAD free energy models in the Fe–Cr-Ni system, polynomial functions are fitted with temperature dependent coefficients represented by Fourier series to accurately describe the phase stability of both fcc and bcc phases in the composition and temperature space. This enables simulations of non-isothermal HIP cycles. Diffusivity data from commercial software and literature are taken to parameterize the kinetic parameters. A discrete nucleation model is incorporated for possible phase transformation. The modified thermodynamic models are validated against previous experiments at 923 K and 1273 K. The interdiffusion kinetics are benchmarked against new HIP experiments joining powder and bulk 316L to bulk SA508 with three different HIP cycles. The good agreement between simulations and experiments on both phase stability and interdiffusion indicate that the model is suitable for simulating interdiffusion between Fe–Cr–Ni alloys during HIP cycles. It is also found that using powder and bulk 316L gives similar interdiffusion profiles at elevated temperature when a dense interface forms during HIP.

Continuous synthesis of metal oxide‐supported high‐entropy alloy nanoparticles with remarkable durability and catalytic activity in the hydrogen reduction reaction

AbstractMetal oxide‐supported multielement alloy nanoparticles are very promising as highly efficient and cost‐effective catalysts with a virtually unlimited compositional space. However, controllable synthesis of ultrasmall multielement alloy nanoparticles (us‐MEA‐NPs) supported on porous metal oxides with a homogeneous elemental distribution and good catalytic stability during long‐term operation is extremely challenging due to their oxidation and strong immiscibility. As a proof of concept that such synthesis can be realized, this work presents a general “bottom‐up” l ultrasonic‐assisted, simultaneous electro‐oxidation–reduction‐precipitation strategy for alloying dissimilar elements into single NPs on a porous support. One characteristic of this technique is uniform mixing, which results from simultaneous rapid thermal decomposition and reduction and leads to multielement liquid droplet solidification without aggregation. This process was achieved through a synergistic combination of enhanced electrochemical and plasma‐chemical phenomena at the metal–electrolyte interface (electron energy of 0.3–1.38 eV at a peak temperature of 3000 K reached within seconds at a rate of ~105 K per second) in an aqueous solution under an ultrasonic field (40 kHz). Illustrating the effectiveness of this approach, the CuAgNiFeCoRuMn@MgO‐P3000 catalyst exhibited exceptional catalytic efficiency in selective hydrogenation of nitro compounds, with over 99% chemoselectivity and nearly 100% conversion within 60 s and no decrease in catalytic activity even after 40 cycles (>98% conversion in 120 s). Our results provide an effective, transferable method for rationally designing supported MEA‐NP catalysts at the atomic level and pave the way for a wide variety of catalytic reactions.image

Large magnetocaloric effect and negative thermal expansion of Mn-Ni-Si-Fe-Co-Ge high-entropy alloys

Magnetocaloric high-entropy alloys (HEAs) with a vast compositional space have attracted significant interest for some time due to their great potential in multifunctional application. However, magnetocaloric HEAs generally exhibit modest magnet-entropy change as their undergo second-order phase transition. This work focuses on designing MM'X (M, M′ = transition metals, X = main group elements) HEAs undergoing magnerostructural transition to achieve great magnetocaloric response. For this purpose, MnxFe1-xNi0.6Co0.4Si0.62Ge0.38 (x = 0.43, 0.45, 0.48, 0.50) with intriguingly crystallographic texture are successfully synthesized and their magnetocaloric effect and negative thermal expansion (NTE) are investigated. The substitution of Fe for Mn reduces the transition temperature (Tt) so that weakens the first-order transition of MM'X HEAs. Besides, Mn0.55Fe0.45Ni0.6Co0.4Si0.62Ge0.38 undergoing first-order transition from Ni2In-paramagnetic-austenitic phase to TiNiSi-ferromagnetic-martensitic phase exhibits a great magnetic entropy change of 16 J∙kg−1K−1 at 206 K for 2 T. Moreover, an obviously NTE is observed through phase transition with liner NTE coefficient of −6.77×10−6 K−1. In addition, Mn0.55Fe0.5Ni0.6Co0.4Si0.62Ge0.38 displays an intriguing NTE with liner NTE coefficient of −1401.7×10−6 K−1 (220–270 K) along the direction parallel to field in a temperature range from 220 K to 270 K, which is much higher than typical NTE materials. Therefore, the present work demonstrates that MM'X HEAs exhibit potential to achieve some magnetocaloric and NTE multifunctional application.

Accelerated discovery of eutectic compositionally complex alloys by generative machine learning

Eutectic alloys have garnered significant attention due to their promising mechanical and physical properties, as well as their technological relevance. However, the discovery of eutectic compositionally complex alloys (ECCAs) (e.g. high entropy eutectic alloys) remains a formidable challenge in the vast and intricate compositional space, primarily due to the absence of readily available phase diagrams. To address this issue, we have developed an explainable machine learning (ML) framework that integrates conditional variational autoencoder (CVAE) and artificial neutral network (ANN) models, enabling direct generation of ECCAs. To overcome the prevalent problem of data imbalance encountered in data-driven ECCA design, we have incorporated thermodynamics-derived data descriptors and employed K-means clustering methods for effective data pre-processing. Leveraging our ML framework, we have successfully discovered dual- or even tri-phased ECCAs, spanning from quaternary to senary alloy systems, which have not been previously reported in the literature. These findings hold great promise and indicate that our ML framework can play a pivotal role in accelerating the discovery of technologically significant ECCAs.

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.