2026-10-14T19:43:26Z https://api.figshare.com/v2/oai

oai:figshare.com:article/34020645 2026-09-29T03:12:46Z category_146 category_7 category_272 category_873 category_734 category_931 category_132 category_135 portal_63 item_type_6 month_year_09_2026

Surface-Periodicity-Guided Structure Mapping for Automated Scanning Tunneling Microscopy Shujun Li (176557) Bingrui Li (4285327) Shiyang Chen (6680855) Xuefeng Wu (106845) Kedong Wang (1824325) Fangfei Ming (2166952) Shaozhi Deng (1425241) Space Science Medicine Environmental Sciences not elsewhere classified Chemical Sciences not elsewhere classified Biological Sciences not elsewhere classified Information Systems not elsewhere classified Infectious Diseases Computational Biology representation enables data local structures must efficient supervised recognition demonstrating autonomous scanning intrinsic reference frame ag adsorption configurations automated stm operations guided structure mapping common lattice frame use surface periodicity crystalline surface periodicity resolved stm images reference images using ag resolved surfaces transferable basis target identification supports environment specific characterization site selection results show registered unit neighborhood relationships model system key challenge generation materials cell maps aware target Automating scanning tunneling microscopy (STM) is essential for scaling atomically precise characterization and, ultimately, fabrication of next-generation materials and devices. A key challenge is to maintain atomic precision across extended surface regions, where local structures must be aligned, identified, and revisited in a common lattice frame. Here we use surface periodicity as an intrinsic reference frame to transform lattice-resolved STM images into lattice-registered unit-cell maps. Using Ag/Si(111)-(7×7) as a model system, the framework preserves lattice indices and neighborhood relationships while standardizing local structural units. This representation enables data-efficient supervised recognition of Ag adsorption configurations from one or a few reference images and supports environment-aware target-site selection. We further implement the framework in a continuous automated STM workflow, demonstrating autonomous scanning, target identification, and structure-specific characterization. Our results show that crystalline surface periodicity can provide a transferable basis for automated STM operations on lattice-resolved surfaces. 2026-09-28T00:00:00Z Text Journal contribution 10.1021/acs.nanolett.6c03145.s001 https://figshare.com/articles/journal_contribution/Surface-Periodicity-Guided_Structure_Mapping_for_Automated_Scanning_Tunneling_Microscopy/34020645 CC BY-NC 4.0