Abstract
Studying defects and defect clusters in reactor materials is essential for understanding the degradation mechanisms of materials under irradiation. By uncovering the formation and evolution of defects, this paper provides critical insights for enhancing the radiation resistance of reactor materials and extending their service life. The contributions of this study are as follows: (1) Integration of a large-scale molecular dynamics (MD) dataset generated by cascade collisions and the application of machine learning (ML) techniques to investigate point defects and their clusters in reactor pressure vessel (RPV) materials; (2) Proposal of a novel clustering method based on physical characteristics, enabling efficient classification of defect clusters while effectively reducing data noise; (3) Development of a component-based defect cluster configuration recognition method using a dual-pointer lattice-filling technique, accurately capturing all defects and identifying several cluster morphologies observed in experiments; (4) Demonstration of the algorithm's outstanding performance in handling systems containing millions of atomic coordinates, showcasing its scalability and robustness; (5) Visualization of the three-dimensional spatial distribution of defect clusters and provision of two-dimensional spatial density distribution maps for vacancy clusters and interstitial clusters, offering precise characterization of spatial relationships within defect clusters and new insights into the irradiation mechanisms of materials.