Abstract
Automatic segmentation of key microstructural features in atomic-scale electron microscope images is critical to improved understanding of structure–property relationships in many important materials and chemical systems. However, the present paradigm involves time-intensive manual analysis that is inherently biased, error-prone, and unable to accommodate the large volumes of data produced by modern instrumentation. While more automated approaches have been proposed, many are not robust to a high variety of data, and do not generalize well to diverse microstructural features and material systems. Here, we present a flexible, semi-supervised few-shot machine learning approach for segmentation of scanning transmission electron microscopy images of three oxide material systems: (1) epitaxial heterostructures of SrTiO3/Ge, (2) La0.8Sr0.2FeO3 thin films, and (3) MoO3 nanoparticles. We demonstrate that the few-shot learning method is more robust against noise, more reconfigurable, and requires less data than conventional image analysis methods. This approach can enable rapid image classification and microstructural feature mapping needed for emerging high-throughput characterization and autonomous microscope platforms.
Highlights
Material microstructures govern the functionality of many important technologies, including catalysts, energy storage devices, and emerging quantum computing architectures
While this approach is suitable for measuring a limited number of microstructural features in small data volumes, it is impractical for samples possessing high density, rare, or noisy features[6,7]
We demonstrate that with only 5–8 sub-images that represent examples of a specific microstructural feature, our model yields segmentation results comparable to those produced by a domain expert for all systems studied here
Summary
Material microstructures govern the functionality of many important technologies, including catalysts, energy storage devices, and emerging quantum computing architectures. Classification tasks have been performed to either assign a label to an entire image that represents a material or microstructure class (e.g., dendritic, equiaxed, etc.)[26,27,28,29], or to assign a label to each pixel in the image so that they are classified into discrete categories[25,30,31,32] The latter classification type, categorization of pixels in an image to identify local features (e.g., line defects, phases, crystal structures), is referred to as segmentation. Motivated by the ability of humans, and especially children, to learn novel visual concepts with sufficient previous knowledge[40] one-shot or few-shot approaches allow human-level performance with fewer and less intensively labeled images (i.e., shots) and little to no training[41,42], but there are limited studies on such methods in the materials science domain[36].
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