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

Abstract

Neutron irradiation produces, within a few picoseconds, displacement cascades that are sequences of atomic collisions generating point and extended defects which subsequently affects the long-term evolution of materials. The diversity of these defects, characterized morphologically and statistically, defines what is called the "primary damage". In this work, we present a fully unsupervised machine learning (ML) workflow that detects and classifies these defects directly from molecular dynamics data. Local environments are encoded by the Smooth Overlap of Atomic Positions (SOAP) vector, anomalous atoms are isolated with autoencoder neural networks (AE), embedded with Uniform Manifold Approximation and Projection (UMAP) and clustered using Hierarchical Density-Based Spatial Clustering of Applications with Noise (HDBSCAN). Applied to 80 keV displacement cascades in Ni, Fe${}_7$0Ni${}_{10}$Cr${}_{20}$, and Zr, the AE successfully identify the small fraction of outlier atoms that participate in defect formation. HDBSCAN then partitions the UMAP latent space of AE-flagged SOAP descriptors into well defined groups representing vacancy- and interstitial-dominated regions and, within each, separates small from large aggregates, assigning 99.7 % of outliers to compact physical motifs. A signed cluster-identification score confirms this separation, and cluster size scales with net defect counts (R2 > 0.89). Statistical cross analyses between the ML outlier map and several conventional detectors (centrosymmetry, dislocation extraction, etc.) reveal strong overlap and complementary coverage, all achieved without template or threshold tuning. This ML workflow thus provides an efficient tool for the quantitative mapping of structural anomalies in materials, particularly those arising from irradiation damage in displacement cascades.

22 pages, 1 graphical abstract, 7 figures, 4 tables

References in corpus (10)

• On representing chemical environments
• Robust Structural Identification via Polyhedral Template Matching
• Accurate determination of crystal structures based on averaged local bond order parameters
• DScribe: Library of Descriptors for Machine Learning in Materials Science
• Insightful classification of crystal structures using deep learning
• A Topological Framework for Local Structure Analysis in Condensed Matter
• Unsupervised learning for local structure detection in colloidal systems
• Updates to the DScribe Library: New Descriptors and Derivatives
• Pattern Matching and Classification of Clusters in Collision Cascades
• Unsupervised learning of representative local atomic arrangements in molecular dynamics data