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The microstructure evolution of Al0.3FeCoNiCrx (x = 0, 0.3, 0.5, 1, 1.5) high-entropy alloys (HEAs) were studied using X-Ray diffraction technique and scanning electron microscope equipped with energy dispersive spectrometer. The short-term and long-term corrosion behaviours of these alloys in 3.5 wt.% NaCl solution were studied by electrochemical impedance spectroscopy, potentiodynamic polarisation measurement, immersion test and corrosion morphology analysis. The results show that all the designed HEAs present single-phase FCC structure, and the increase in Cr content changes the microstructural morphology from cellular to a typical dendritic–interdendritic state. Without the influence of phase transformation, the corrosion resistance of Al0.3FeCoNiCrx HEAs gradually increases with the increase in Cr content. Our designed alloys exhibit excellent corrosion resistance compared to the existing HEAs in the AlFeCoNiCr composition system. [ABSTRACT FROM AUTHOR]

In recent years, the detrimental impact of traditional gas–liquid refrigerants on the environment has prompted a shift towards sustainable solid-state refrigeration technology. The elastocaloric effect, particularly in NiTi-based shape memory alloys (SMAs), presents a promising alternative due to its high coefficient of performance. However, conventional methods for alloy development are inefficient, often failing to meet the stringent requirements for practical applications. This study employed machine learning (ML) to accelerate the design of NiTi-based SMAs with an enhanced elastocaloric effect. Through active learning across four iterations, we identified nine novel NiTi-based SMAs exhibiting phase-transformation-induced entropy changes (ΔS) greater than 90 J/kg·K−1, surpassing most existing alloys. Our ML model demonstrates robust interpretability, revealing key relationships between material features and performance. This work not only establishes a more efficient pathway for alloy discovery but also aims to contribute significantly to the advancement of sustainable refrigeration technologies. [ABSTRACT FROM AUTHOR]

The deep learning-based image segmentation approach has evolved into the mainstream of target detection and shape characterization in microscopic image analysis. However, the accuracy and generalizability of deep learning approaches are still hindered by the insufficient data problem that results from the high expense of human and material resources for microscopic image acquisition and annotation. Generally, image augmentation can increase the amount of data in a short time by means of mathematical simulation, and has become a necessary module for deep learning-based material microscopic image analysis. In this work, we first review the commonly used image augmentation methods and divide more than 60 basic image augmentation methods into eleven categories based on different implementation strategies. Secondly, we conduct experiments to verify the effectiveness of various basic image augmentation methods for the image segmentation task of two classical material microscopic images using evaluation metrics with different applicabilities. The U-Net model was selected as a representative benchmark model for image segmentation tasks, as it is the classic and most widely used model in this field. We utilize this model to verify the improvement of segmentation performance by various augmentation methods. Then, we discuss the advantages and applicability of various image augmentation methods in the material microscopic image segmentation task. The evaluation experiments and conclusions in this work can serve as a guide for the creation of intelligent modeling frameworks in the materials industry. [ABSTRACT FROM AUTHOR]

Simulating the frictional properties of complex interfaces is computational resource consuming. In this paper, we propose a density functional theory (DFT) calculation combined machine learning (ML) strategy to investigate the sliding potential energy corrugation between geometrical corrugated graphene (Gr) sheets. By the aid of few DFT calculations and geometrical descriptors Σr−n (n = 1, 2, 6, 12), the trained ML models can accurately predict the sliding potential evolutions of Gr/Pt and Gr/Re systems. To be specific, based on DFT calculations of sliding along [110] direction, the trained linear regression (LIN) models can properly give out the potential energy evolution along the [100] direction with deviation less than 5%. By the dataset of given distances (9.3 Ĺ, 9.65 Ĺ and 10 Ĺ) between two Re monolayers in Gr/Re systems, LIN and Bayesian ridge regression (BR) models can quantitatively predict the potential energy evolution of unknown distances (9.2 Ĺ, 9.4 Ĺ, 9.5 Ĺ and 9.6 Ĺ). The predicted magnitudes of potential energy corrugations by BR model divert less than 3 meV Ĺ−2 from DFT calculations. The prediction results for extrapolated distances (9.0 Ĺ and 9.1 Ĺ) deviate notably, but the extension of training dataset effectively improves the predictive ability of ML models, especially for the LIN model. Thus, the supposed strategy could become an effective method to investigate the frictional characteristics of complex interfaces. [ABSTRACT FROM AUTHOR]

Summary: The microscopic image is important data for recording the microstructure information of materials. Researchers usually use image‐processing algorithms to extract material features from that and then characterise the material microstructure. However, the microscopic images obtained by a microscope often have random damaged regions, which will cause the loss of information and thus inevitably influence the accuracy of microstructural characterisation, even lead to a wrong result. To handle this problem, we provide a deep learning‐based fully automatic method for detecting and inpainting damaged regions in material microscopic images, which can automatically inpaint damaged regions with different positions and shapes, as well as we also use a data augmentation method to improve the performance of inpainting model. We evaluate our method on Al–La alloy microscopic images, which indicates that our method can achieve promising performance on inpainted and material microstructure characterisation results compared to other image inpainting software for both accuracy and time consumption. Lay Description: A basic goal of materials data analysis is to extract useful information from materials datasets that can in turn be used to establish connections along the composition–processing–structure–properties chain. The microscopic images obtained by a microscope is the key carrier of material microstructural information. Researchers usually use image analysis algorithms to extract regions of interest or useful features from microscopic images, aiming to analyse material microstructure, organ tissues or device quality etc. Therefore, the integrity and clarity of the microscopic image are the most important attributes for image feature extraction. Scientists and engineers have been trying to develop various technologies to obtain perfect microscopic images. However, in practice, some extrinsic defects are often introduced during the preparation and/or shooting processes, and the elimination of these defects often requires mass efforts and cost, or even is impossible at present. Take the microstructure image of metallic material for example, samples prepared to microstructure characterisation often need to go through several steps such as cutting, grinding with sandpaper, polishing, etching, and cleaning. During the grinding and polishing process, defects such as scratches could be introduced. During the etching and cleaning process, some defects such as rust caused by substandard etching, stains etc. may arise and be persisted. These defects can be treated as damaged regions with nonfixed positions, different sizes, and random shapes, resulting in the loss of information, which seriously affects subsequent visual observation and microstructural feature extraction. To handle this problem, we provide a deep learning‐based fully automatic method for detecting and inpainting damaged regions in material microscopic images, which can automatically inpaint damaged regions with different positions and shapes, as well as we also use a data augmentation method to improve the performance of inpainting model. We evaluate our method on Al–La alloy microscopic images, which indicates that our method can achieve promising performance on inpainted and material microstructure characterisation results compared to other image inpainting software for both accuracy and time consumption. [ABSTRACT FROM AUTHOR]

Quantitative analysis through image processing is a key step to gain information regarding the microstructure of materials. In this paper, we develop a deep learning-based method to address the task of image segmentation for microscopic images using an Al–La alloy. Our work makes three key contributions. (1) We train a deep convolutional neural network based on DeepLab to achieve image segmentation and have significant results. (2) We adopt a local processing method based on symmetric overlap-tile strategy which makes it possible to analyze the microscopic images with high resolution. Additionally, it achieves seamless segmentation. (3) We apply symmetric rectification to enhance the accuracy of results with 3D information. Experimental results showed that our method outperforms existing segmentation methods. [ABSTRACT FROM AUTHOR]