{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/manifold-learning-of-four-dimensional","title":"Manifold Learning of Four-dimensional Scanning Transmission Electron Microscopy","arxiv_id":"1811.00080","date":"2018-10-18","proceeding":null,"authors":["Xin Li","Ondrej E. Dyck","Mark P. Oxley","Andrew R. Lupini","Leland McInnes","John Healy","Stephen Jesse","Sergei V. Kalinin"],"abstract":"Four-dimensional scanning transmission electron microscopy (4D-STEM) of local\natomic diffraction patterns is emerging as a powerful technique for probing\nintricate details of atomic structure and atomic electric fields. However,\nefficient processing and interpretation of large volumes of data remain\nchallenging, especially for two-dimensional or light materials because the\ndiffraction signal recorded on the pixelated arrays is weak. Here we employ\ndata-driven manifold leaning approaches for straightforward visualization and\nexploration analysis of the 4D-STEM datasets, distilling real-space neighboring\neffects on atomically resolved deflection patterns from single-layer graphene,\nwith single dopant atoms, as recorded on a pixelated detector. These extracted\npatterns relate to both individual atom sites and sublattice structures,\neffectively discriminating single dopant anomalies via multi-mode views. We\nbelieve manifold learning analysis will accelerate physics discoveries coupled\nbetween data-rich imaging mechanisms and materials such as ferroelectric,\ntopological spin and van der Waals heterostructures.","url_abs":"http://arxiv.org/abs/1811.00080v3","url_pdf":"http://arxiv.org/pdf/1811.00080v3.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"manifold-learning-of-four-dimensional","repo_url":"https://github.com/nonmin/4D-STEM","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1811.00080","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}