Crystalline materials exhibit long-range ordered lattice unit, within which resides nonperiodic structural features called defects. These crystallographic defects play a vital role in determining the physical and mechanical properties of a wide range of material systems. While computer vision has demonstrated success in recognizing feature patterns in images with well-defined contrast, automated identification of nanometer scale crystallographic defects in electron micrographs governed by complex contrast mechanisms is still a challenging task. Here, building upon an advanced defect imaging mode that offers high feature clarity, we introduce DefectSegNet - a new convolutional neural network (CNN) architecture that performs semantic segmentation of three common crystallographic defects in structural alloys: dislocation lines, precipitates and voids. Results from supervised training on a small set of high-quality defect images of steels show high pixel-wise accuracy across all three types of defects: 91.60 ± 1.77% on dislocations, 93.39 ± 1.00% on precipitates, and 98.85 ± 0.56% on voids. We discuss the sources of uncertainties in CNN prediction and the training data in terms of feature density, representation and homogeneity and their effects on deep learning performance. Further defect quantification using DefectSegNet prediction outperforms human expert average, presenting a promising new workflow for fast and statistically meaningful quantification of materials defects.

High-temperature oxidation mechanisms of metallic nanoparticles have been extensively investigated; however, it is challenging to determine whether the kinetic modeling is applicable at the nanoscale and how the differences in nanoparticle size influence the oxidation mechanisms. In this work, we study thermal oxidation of pristine Ni nanoparticles ranging from 4 to 50 nm in 1 bar 1%O2/N2 at 600 °C using in situ gas-cell environmental transmission electron microscopy. Real-space in situ oxidation videos revealed an unexpected nanoparticle surface refacetting before oxidation and a strong Ni nanoparticle size dependence, leading to distinct structural development during the oxidation and different final NiO morphology. By quantifying the NiO thickness/volume change in real space, individual nanoparticle-level oxidation kinetics was established and directly correlated with nanoparticle microstructural evolution with specified fast and slow oxidation directions. Thus, for the size-dependent Ni nanoparticle oxidation, we propose a unified oxidation theory with a two-stage oxidation process: stage 1: dominated by the early NiO nucleation (Avrami–Erofeev model) and stage 2: the Wagner diffusion-balanced NiO shell thickening (Wanger model). In particular, to what extent the oxidation would proceed into stage 2 dictates the final NiO morphology, which depends on the Ni starting radius with respect to the critical thickness under given oxidation conditions. The overall oxidation duration is controlled by both the diffusivity of Ni2+ in NiO and the Ni in Ni self-diffusion. We also compare the single-particle kinetic curve with the collective one and discuss the effects of nanoparticle size differences on kinetic model analysis.

In-situ irradiation transmission electron microscopy (TEM) offers unique insights into the millisecond-timescale post-cascade process, such as the lifetime and thermal stability of defect clusters, vital to the mechanistic understanding of irradiation damage in nuclear materials. Converting in-situ irradiation TEM video data into meaningful information on defect cluster dynamic properties (e.g., lifetime) has become the major technical bottleneck. Here, we present a solution called the DefectTrack, the first dedicated deep learning-based one-shot multi-object tracking (MOT) model capable of tracking cascade-induced defect clusters in in-situ TEM videos in real-time. DefectTrack has achieved a Multi-Object Tracking Accuracy (MOTA) of 66.43% and a Mostly Tracked (MT) of 67.81% on the test set, which are comparable to state-of-the-art MOT algorithms. We discuss the MOT framework, model selection, training, and evaluation strategies for in-situ TEM applications. Further, we compare the DefectTrack with four human experts in quantifying defect cluster lifetime distributions using statistical tests and discuss the relationship between the material science domain metrics and MOT metrics. Our statistical evaluations on the defect lifetime distribution suggest that the DefectTrack outperforms human experts in accuracy and speed.

The current practice of identifying defects in microscopy images and deriving metrics such as dislocation density and precipitates/voids diameter remains largely in the purview of human analysis. The lack of automated defect analysis for statistically meaningful quantification of a variety types of crystallographic defects is causing an increasingly large bottleneck for rational alloy design. The first and most important step of automating defect analysis is perceptual defect identification. In terms of digital image processing, semantic segmentation best emulates human recognition of defect features -it tells what defects are in an image and where they are located. In this work, we developed a novel deep convolutional neural network (CNN) model, called DefectNet, for robust and automated semantic segmentation of three crystallographic defects including line dislocations, precipitates, and voids commonly observed in structural metals and alloys [1]. Defect semantic segmentation in TEM micrographs is a challenging deep learning task due to the nature of the image itself. Unlike everyday photographs, the interpretation of image contrast in TEM micrographs is usually not straightforward; multiple contrast mechanisms often contribute to the observation of defect features. Here, we aim at resolving this image-induced challenge by optimizing the image quality. In previous work, we established an experimental protocol for a diffraction contrast imaging scanning transmission electron microscopy (DCI STEM) technique and tailored it specifically for imaging defects in popular iron-based structural alloys [2]. Thus, the DefectNet was trained on a small set of high-quality DCI STEM defect images obtained from HT-9 martensitic steels. The performance of the resulting model for each defect was assessed quantitatively by standard semantic segmentation evaluation metrics, and the resulting defect density and size measurements compared to that from a group of human experts. Figure 1 presents the DefectNet semantic segmentation predictions for the development and test sets of the three crystallographic defects. Compared to the ground truth label, the deep learning predicted defect maps show excellent resemblance. In the comparison maps color-coded by the confusion matrix at the pixel level, we can see that the majority of pixels in the prediction map are in black and turquoise and thus correctly classified as the background and the corresponding defect. Table 1 summarizes the semantic segmentation performance of the DefectNet on the test sets. The current DefectNet was trained over a limited number of labeled DCI STEM images, but it has achieved an excellent prediction performance on the test sets, with an overall averaged pixel accuracy of 94.61±1.13%, precision of 72.12±2.73%, recall of 79.22±3.27%, and region intersection over union (IU) of 61.79±2.13%, comparable to state-of-the-art deep learning semantic segmentation algorithm. The success and source of error in DCNN prediction was analyzed for each defect features in terms o...

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