DINO4DSTEM: A self-supervised framework for structural discovery in 4D-STEM

[2608.15098] DINO4DSTEM: A self-supervised framework for structural discovery in 4D-STEM Skip to main content Search Submit Donate Log in Search arXiv Press Enter to search · Advanced search Condensed Matter > Materials Science arXiv:2608.15098 (cond-mat) [Submitted on 15 Aug 2026] Title: DINO4DSTEM: A self-supervised framework for structural discovery in 4D-STEM Authors: Daniel Khaykelson , Lothar Houben , Boris Rybtchinski View a PDF of the paper titled DINO4DSTEM: A self-supervised framework for structural discovery in 4D-STEM, by Daniel Khaykelson and 2 other authors View PDF Abstract: Nanodiffraction using 4D-STEM has become a key technique for quantitative nanoscale structural mapping in materials research, yet interpreting its high-dimensional datasets in structurally complex materials remains a major bottleneck. Existing analysis workflows typically rely on structural models, manual annotation, predefined classes, or sample-specific heuristics, limiting their ability to characterize heterogeneous complex materials. Here, we introduce DINO4DSTEM, a self-supervised machine learning framework that automatically discovers structurally meaningful states directly from raw diffraction data. Without structural models, manual labels, a predefined number of classes, or system-specific parameter tuning, the framework learns representations that organize diffraction patterns by their intrinsic structural similarities, transforming large collections of low-dose measurements into quantitative nanoscale structure maps. Across diverse datasets, DINO4DSTEM consistently identifies the dominant structural degrees of freedom, providing segmentation without human supervision. We applied the framework to the crystallization of indomethacin, a beam-sensitive, multidomain pharmaceutical system, revealing that crystallinity emerges from a partially ordered precursor and spans a continuous spectrum of structural order. The discovered nanoscale structural states are mapped to reveal the evolution of order across the specimen quantitatively. By replacing task-specific analysis with general self-supervised representation learning, DINO4DSTEM provides a broadly applicable framework for quantitative nanoscale structural mapping in complex materials, enabling the discovery of emergent structural organization in heterogeneous, beam-sensitive systems. Comments: Main text: 17 pages, 6 figures. Includes Supplementary Information (14 pages, 20 supplementary figures). Code: this https URL Subjects: Materials Science (cond-mat.mtrl-sci) ; Applied Physics (physics.app-ph) Cite as: arXiv:2608.15098 [cond-mat.mtrl-sci] (or arXiv:2608.15098v1 [cond-mat.mtrl-sci] for this version) https://doi.org/10.48550/arXiv.2608.15098 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Daniel Khaykelson [ view email ] [v1] Sat, 15 Aug 2026 07:48:04 UTC (3,808 KB) Full-text links: Access Paper: View a PDF of the paper titled DINO4DSTEM: A self-supervised framework for structural discovery in 4D-STEM, by Daniel Khaykelson and 2 other authors View PDF view license Current browse context: cond-mat.mtrl-sci < prev | next > new | recent | 2026-08 Change to browse by: cond-mat physics physics.app-ph References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... 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