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Accurately quantifying swelling of alloys that have undergone irradiation isessential for understanding alloy performance in a nuclear reactor and criticalfor the safe and reliable operation of reactor facilities. However, typicalpractice is for radiation-induced defects in electron microscopy images ofalloys to be manually quantified by domain-expert researchers. Here, we employan end-to-end deep learning approach using the Mask Regional ConvolutionalNeural Network (Mask R-CNN) model to detect and quantify nanoscale cavities inirradiated alloys. We have assembled the largest database of labeled cavityimages to date, which includes 400 images, >34k discrete cavities, and numerousalloy compositions and irradiation conditions. We have evaluated bothstatistical (precision, recall, and F1 scores) and materials property-centric(cavity size, density, and swelling) metrics of model performance, andperformed in-depth analysis of materials swelling assessments. We find ourmodel gives assessments of material swelling with an average (standarddeviation) swelling mean absolute error based on random leave-outcross-validation of 0.30 (0.03) percent swelling. This result demonstrates ourapproach can accurately provide swelling metrics on a per-image andper-condition basis, which can provide helpful insight into material design(e.g., alloy refinement) and impact of service conditions (e.g., temperature,irradiation dose) on swelling. Finally, we find there are cases of test imageswith poor statistical metrics, but small errors in swelling, pointing to theneed for moving beyond traditional classification-based metrics to evaluateobject detection models in the context of materials domain applications.

Materials Swelling Revealed Through Automated Semantic Segmentation of Cavities in Electron Microscopy Images