WPU-Net: Boundary Learning by Using Weighted Propagation in Convolution Network

Deep learning has driven a great progress in natural and biological image processing. However, in material science and engineering, there are often some flaws and indistinctions in material microscopic images induced from complex sample preparation, even due to the material itself, hindering the detection of target objects. In this work, we propose WPU-net that redesigns the architecture and weighted loss of U-Net, which forces the network to integrate information from adjacent slices and pays more attention to the topology in boundary detection task. Then, the WPU-net is applied into a typical material example, i.e., the grain boundary detection of polycrystalline material. Experiments demonstrate that the proposed method achieves promising performance and outperforms state-of-the-art methods. Besides, we propose a new method for object tracking between adjacent slices, which can effectively reconstruct 3D structure of the whole material. Finally, we present a material microscopic image dataset with the goal of advancing the state-of-the-art in image processing for material science.Comment: technical repor

Predicting Transition Temperature of Superconductors with Graph Neural Networks

Predicting high temperature superconductors has long been a great challenge. The difficulty lies in how to predict the transition temperature (Tc) of superconductors. Although recent progress in material informatics has led to a number of machine learning models predicting Tc, prevailing models have not shown adequate generalization ability and physical rationality to find new high temperature superconductors, yet. In this work, a bond sensitive graph neural network (BSGNN) was developed to predict the Tc of various superconductors. In BSGNN, communicative message passing and graph attention methods were utilized to enhance the model's ability to process bonding and interaction information in the crystal lattice, which is crucial for the superconductivity. Consequently, our results revealed the relevance between chemical bond attributes and Tc. It indicates that shorter bond length is favored by high Tc. Meanwhile, some specific chemical elements that have relatively large van der Waals radius is favored by high Tc. It gives a convenient guidance for searching high temperature superconductors in materials database, by ruling out the materials that could never have high Tc

Effects of Strain Rate and Measuring Temperature on the Elastocaloric Cooling in a Columnar-Grained Cu71Al17.5Mn11.5 Shape Memory Alloy

Solid-state refrigeration technology based on elastocaloric effects (eCEs) is attracting more and more attention from scientists and engineers. The response speed of the elastocaloric materials, which relates to the sensitivity to the strain rate and measuring temperature, is a significant parameter to evaluate the development of the elastocaloric material in device applications. Because the Cu-Al-Mn shape memory alloy (SMA) possesses a good eCE and a wide temperature window, it has been reported to be the most promising elastocaloric cooling material. In the present paper, the temperature changes (ΔT) induced by reversible martensitic transformation in a columnar-grained Cu71Al17.5Mn11.5 SMA fabricated by directional solidification were directly measured over the strain rate range of 0.005–0.19 s−1 and the measuring temperature range of 291–420 K. The maximum adiabatic ΔT of 16.5 K and a lower strain-rate sensitivity compared to TiNi-based SMAs were observed. With increasing strain rate, the ΔT value and the corresponding coefficient of performance (COP) of the alloy first increased, then achieved saturation when the strain rate reached 0.05 s−1. When the measuring temperature rose, the ΔT value increased linearly while the COP decreased linearly. The results of our work provide theoretical reference for the design of elastocaloric cooling devices made of this alloy

The γ/γ′ microstructure in CoNiAlCr-based superalloys using triple-objective optimization

Abstract Optimizing several properties simultaneously based on small data-driven machine learning in complex black-box scenarios can present difficulties and challenges. Here we employ a triple-objective optimization algorithm deduced from probability density functions of multivariate Gaussian distributions to optimize the γ′ volume fraction, size, and morphology in CoNiAlCr-based superalloys. The effectiveness of the algorithm is demonstrated by synthesizing alloys with desired γ/γ′ microstructure and optimizing γ′ microstructural parameters. In addition, the method leads to incorporating refractory elements to improve γ/γ′ microstructure in superalloys. After four iterations of experiments guided by the algorithm, we synthesize sixteen alloys of relatively high creep strength from ~120,000 candidates of which three possess high γ′ volume fraction (>54%), small γ′ size (77%)

DO22-(Cu,Ni)(3)Sn intermetallic compound nanolayer formed in Cu/Sn-nanolayer/Ni structures

The present work conducts crystal characterization by High Resolution Transmission Electron Microscopy (HRTEM) on Cu/Sn-nanolayer/Ni sandwich structures associated with the use of Energy Dispersive X-ray (EDX) analysis. The results show that DO22-(Cu,Ni)(3)Sn intermetallic compound (IMC) ordered structure is formed in the sandwich structures at the as-electrodeposited state. The formed DO22-(Cu,Ni)(3)Sn IMC is a homogeneous layer with a thickness about 10 nm. The DO22-(Cu,Ni)(3)Sn IMC nanolayer is stable during annealing at 250 degrees C for 810 min. The formation and stabilization of the metastable DO22-(Cu,Ni)(3)Sn IMC nanolayer are attributed to the less strain energy induced by lattice mismatch between the DO22 IMC and fcc Cu crystals in comparison with that between the equilibrium DO3 IMC and fcc Cu crystals. (C) 2009 Elsevier B.V. All rights reserved

Deep Learning-Based Image Segmentation for Al-La Alloy Microscopic Images

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

Growth and Photocatalytic Activity of Dendrite-like ZnO@Ag Heterostructure Nanocrystals

Dendrite-like ZnO@Ag heterostructure nanocrystals are designed and fabricated by a facial two-step chemical method in a large scale. The heterostructure nanocrystals are composed of single crystal Ag nanowires as trunks and highly dense (0001) oriented ZnO nanorods as branches. ZnO nanorods with diameters of about 50-400 nm are vertically grown on the six lateral surfaces of the Ag nanowires. Ultrathin ZnO nanowires or nanotubes with a diameter of less than 30 rim are decorated on the ZnO nanorods. The photocatalysis test shows that the ZnO@Ag heterostructures exhibit a higher photocatalytic activity than the pure ZnO nanorods, thereby implying that the Ag/ZnO interfaces promote the separation of photogenerated electron-hole pairs and enhance the photocatalytic activity

A fast algorithm for material image sequential stitching

In material research, it is often highly desirable to observe images of whole microscopic sections with high resolution. So that micrograph stitching is an important technology to produce a panorama or larger image by combining multiple images with overlapping areas, while retaining microscopic resolution. However, due to high complexity and variety of microstructure, most traditional methods could not balance speed and accuracy of stitching strategy. To overcome this problem, we develop a method named very fast sequential micrograph stitching (VFSMS), which employ incremental searching strategy and GPU acceleration to guarantee the accuracy and the speed of stitching results. Experimental results demonstrate that the VFSMS achieve state-of-art performance on three types’ microscopic datasets on both accuracy and speed aspects. Besides, it significantly outperforms the most famous and commonly used software, such as ImageJ, Photoshop and Autostitch. The software is available at https://www.mgedata.cn/app_entrance/microscope