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Unsupervised Machine-Learning Pipeline for Data-Driven Defect Detection and Characterisation: Application to Displacement Cascades

arXiv — cs.LG•Wednesday, October 29, 2025 at 4:00:00 AM
PositiveArtificial Intelligence
A new unsupervised machine learning pipeline has been developed to enhance defect detection and characterization in materials affected by neutron irradiation. This innovative approach allows for the identification and classification of atomic defects that occur during displacement cascades, which are crucial for understanding material behavior over time. This advancement is significant as it could lead to improved material performance in various applications, particularly in nuclear engineering and materials science.
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