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High entropy alloys (HEAs) and compositionally complex alloys (CCAs) have recently attracted great research interest because of their remarkable mechanical and physical properties. Although many useful HEAs or CCAs were reported, the rules of phase design, if there are any, which could guide alloy screening are still an open issue. In this work, we made a critical appraisal of the existing design rules commonly used by the academic community with different machine learning (ML) algorithms. Based on the artificial neural network algorithm, we were able to derive and extract a sensitivity matrix from the ML modeling, which enabled the quantitative assessment of how to tune a design parameter for the formation of a certain phase, such as solid solution, intermetallic, or amorphous phase. Furthermore, we explored the use of an extended set of new design parameters, which had not been considered before, for phase design in HEAs or CCAs with the ML modeling. To verify our ML-guided design rule, we performed various experiments and designed a series of alloys out of the Fe-Cr-Ni-Zr-Cu system. The outcomes of our experiments agree reasonably well with our predictions, which suggests that the ML-based techniques could be a useful tool in the future design of HEAs or CCAs.

Considering the impact of environmental pollution and market competition on the business model of enterprises, a method of constructing sustainable supply chain network model under the price competition environment is proposed to achieve the balance of economic benefit, ecological benefit, environmental benefit and social benefit. Firstly, based on the concepts of sustainability and price competition, the model with maximum total network profit, minimum carbon emission and maximum social benefit is designed. Secondly, based on fuzzy programming theory, an expected value fuzzy chance constrained programming model with confidence measure is constructed to address the challenge of designing a sustainable closed-loop supply chain network in the face of uncertain conditions. Thirdly, the problems of premature convergence and slow convergence during the traditional particle swarm optimization algorithm and genetic algorithm are solved with the particle chaotic ant colony algorithm (PSCACO). Finally, taking a manufacturing enterprise as an example. By analyzing the different confidence level measures under single objective optimization and multi-objective optimization, sustainable closed-loop supply chain network planning method established is verified on feasibility and effectiveness.

In flexible electronics, the need for ultrathin encapsulation offering a blend of features is crucial. While metal-oxide films are often considered promising candidates, their inherent brittleness has limited their practical utility. Here, we have engineered freestanding fracture-resistant high-entropy-oxide (HEO) nanomembranes by creating an in-situ nano-oxide scaffold within hydrogels. The HEO nanomembranes exhibit ductility nearing 90% and toughness exceeding 300 MJ/m 3 , surpassing traditional metal and metal-oxide films, as well as many advanced 2D materials. These mechanical properties are a result of the dual-phase nanostructure, where the HEO scaffold intertwined with decomposed hydrogel chains provides hierarchical toughening mechanisms that effectively impede and deflect crack propagation. Furthermore, our nanomembranes demonstrate strong adhesion to diverse substrates and impressive optical characteristics, boasting a visible transmittance of 83.2%. Even under high-temperature and humid conditions with a ~ 5% bending strain, the nanomembrane proves effective in preventing oxidation of copper circuits. The authors develop a freestanding high-entropy-oxide (HEO) nanomembrane with exceptional fracture resistance and multifunctionality. Formation of dual-phase nanostructure (HEO scaffold + decomposed hydrogel chains) enables hierarchical toughening.

Superelasticity – exhibiting either Hookean (linear) or non-Hookean (nonlinear) recoverable strain beyond 2% – has been realized in distinct material systems such as metallic glasses, shape memory alloys, strain glass alloys and Gum metals, enabling diverse technological applications. Here we demonstrate that, through compositional tuning in a high-entropy alloy, the elastic behavior can be continuously and reversibly modulated between Hookean superelasticity, non-Hookean superelasticity with an ultrahigh recoverable strain of ~8%, and back to the Hookean regime. By combining atomic-scale strain mapping and extensive first-principles calculations, we reveal that this tunability is governed by a hidden strain order, arising from frustrated crystallization of two competing phases. As a result, local lattice distortion arises, producing a heterogeneous strain landscape that modulates phase stability, phase transformation propensity, and elastic response. Our findings establish a materials design strategy for programming Hookean and non-Hookean elasticity behavior on demand, with promising applications in microelectromechanical systems, high-precision actuators, and adaptive damping devices. Designing superelastic materials remains challenging. Here, the authors reveal that hidden atomic-scale strain order in a high-entropy alloy enables reversible switching between linear and nonlinear superelasticity through subtle composition tuning.

Eutectic and near-eutectic high entropy alloys (HEAs) have recently attracted a great deal of interest because of their promising properties, such as an excellent castability and unique combination of good ductility and high strength. However, in the absence of a phase diagram, it remains a non-trivial task to find a eutectic or near-eutectic composition for a HEA system, which usually demands a tremendous amount of efforts if a trial-and-error approach is followed. In this paper, we briefly review the thermodynamics that governs the eutectic solidification in regular binary and ternary alloys, and proceed to the discussion for the design of eutectic HEAs. Based on the data reported, we then propose an improved strategy which may enable an efficient search for the eutectic or near eutectic HEA compositions.

The well-known strength-toughness trade-off has long been an obstacle in the pursuit of advanced structural alloys. Here, we develop a eutectic high entropy alloy that effectively overcomes this limitation. Our alloy is composed of face-centered cubic and body-centered cubic crystalline phases, and demonstrates attractive mechanical properties by harnessing microstructural hybridization and a strain-induced phase transition between phases. Unlike conventional eutectic alloys, the compositionally complexity of our alloy allows control of its microstructural and chemical heterogeneities across multiple length scales, ranging from atomic- and nano-scales to meso-scales. Optimizing these microstructural and chemical heterogeneities within our alloy enables high strength and ductility because of enhanced fracture resistance, outperforming alternative high and medium entropy alloys with similar compositions and microstructures.Overcoming the strength-toughness trade-off is a key goal of alloy engineering. Here, a two-phase eutectic high entropy alloy is reported that harnesses microstructural and chemical heterogeneity to achieve high toughness and ductility.

Metallic glasses (MGs) have an amorphous atomic arrangement, but their structure and dynamics in the nanoscale are not homogeneous. Numerous studies have confirmed that the static and dynamic heterogeneities of MGs are vital for their deformation mechanism. The “defects” in MGs are envisaged to be structurally loosely packed and dynamically active to external stimuli. To date, no definite structure-property relationship has been established to identify liquid-like “defects” in MGs. In this paper, we proposed a machine-learned “defects” from atomic trajectories rather than static structural signatures. We analyzed the atomic motion behavior at different temperatures via a k-nearest neighbors machine learning model, and quantified the dynamics of individual atoms as the machine-learned temperature. Applying this new temperature-like parameter to MGs under stress-induced flow, we can recognize which atoms respond like “liquids” to the applied loads. The evolution of liquid-like regions reveals the dynamic origin of plasticity (thermo- and acousto-plasticity) of MGs and the correlation between stress-induced heterogeneity and local environment around atoms, providing new insights into thermo- and acousto-plastic forming.

In order to address the impact of the uncertainty and intermittency of a photovoltaic power generation system on the smooth operation of the power system, a microgrid scheduling model incorporating photovoltaic power generation forecast is proposed in this paper. Firstly, the factors affecting the accuracy of photovoltaic power generation prediction are analyzed by classifying the photovoltaic power generation data using cluster analysis, analyzing its important features using Pearson correlation coefficients, and downscaling the high-dimensional data using PCA. And based on the theories of the sparrow search algorithm, convolutional neural network, and bidirectional long- and short-term memory network, a combined SSA-CNN-BiLSTM prediction model is established, and the attention mechanism is used to improve the prediction accuracy. Secondly, a multi-temporal dispatch optimization model of the microgrid power system, which aims at the economic optimization of the system operation cost and the minimization of the environmental cost, is constructed based on the prediction results. Further, differential evolution is introduced into the QPSO algorithm and the model is solved using this improved quantum particle swarm optimization algorithm. Finally, the feasibility of the photovoltaic power generation forecasting model and the microgrid power system dispatch optimization model, as well as the validity of the solution algorithms, are verified through real case simulation experiments. The results show that the model in this paper has high prediction accuracy. In terms of scheduling strategy, the generation method with the lowest cost is selected to obtain an effective way to interact with the main grid and realize the stable and economically optimized scheduling of the microgrid system.

Multi–principal element alloys (MPEAs) exhibit outstanding mechanical properties because the core effect of severe atomic lattice distortion is distinctly different from that of traditional alloys. However, at the mesoscopic scale the underlying physics for the abundant dislocation activities responsible for strength-ductility synergy has not been uncovered. While the Eshelby mean-field approaches become insufficient to tackle yielding and plasticity in severely distorted crystalline solids, here we develop a three-dimensional discrete dislocation dynamics simulation approach by taking into account the experimentally measured lattice strain field from a model FeCoCrNiMn MPEA to explore the heterogeneous strain-induced strengthening mechanisms. Our results reveal that the heterogeneous lattice strain causes unusual dislocation behaviors (i.e., multiple kinks/jogs and bidirectional cross slips), resulting in the strengthening mechanisms that underpin the strength-ductility synergy. The outcome of our research sheds important insights into the design of strong yet ductile distorted crystalline solids, such as highentropy alloys and high-entropy ceramics.

Recent investigations indicate that high entropy alloys (HEAs) may exhibit distinctive thermal characteristics compared to traditional alloys. Through a blend of experiments and atomistic simulations, this study showcases that the lattice thermal conductivity of a highly distorted single-phase B2 alloy with the composition of (CoNi)50(TiZrHf)50 is as low as less than 1 W/(m·K), akin to that of ceramics like alumina, and remains stable across temperatures from 300 to 900 K. This remarkable thermal behavior is attributed to significant lattice distortion and atomic mass variation within this alloy. These findings suggest potential applications for distorted HEAs in thermal insulation technologies tailored for challenging environments.