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Autonomous scanning probe microscopy investigations over WS_(2)and Au{111}

npj Computational Materials 2022年第1期8卷 916-922页

作者： John C.Thomas Antonio Rossi Darian Smalley luca francaviglia Zhuohang Yu Tianyi Zhang Shalini Kumari Joshua A.Robinson Mauricio Terrones Masahiro Ishigami Eli Rotenberg Edward S.Barnard Archana Raja Ed Wong D.Frank Ogletree Marcus M.Noack Alexander Weber-Bargioni Molecular Foundry Lawrence Berkeley National LaboratoryBerkeleyCA94720United States Advanced Light Source Lawrence Berkeley National LaboratoryBerkeleyCA94720United States Department of Physics and NanoScience Technology Center University of Central FloridaOrlandoFL32816United States Department of Materials Science and Engineering The Pennsylvania State UniversityUniversity ParkPA16082United States Center for Two-Dimensional and Layered Materials The Pennsylvania State UniversityUniversity ParkPA16802United States Department of Physics and Department of Chemistry The Pennsylvania State UniversityUniversity ParkPA16802United States Center for Advanced Mathematics for Energy Research Applications Lawrence Berkeley National LaboratoryBerkeleyCA94720United States

Individual atomic defects in 2D materials impact their macroscopic *** the interplay is challenging,however,intelligent hyperspectral scanning tunneling spectroscopy(STS)mapping provides a feasible solution to this technically difficult and time consuming ***,dense spectroscopic volume is collected autonomously via Gaussian process regression,where convolutional neural networks are used in tandem for spectral *** data enable defect segmentation,and a workflow is provided for machine-driven decision making during experimentation with capability for user *** provide a means towards autonomous experimentation for the benefit of both enhanced reproducibility and *** investigations on WS_(2)sulfur vacancy sites are explored,which is combined with local density of states confirmation on the Au{111}herringbone *** vacancies,pristine WS_(2),Au face-centered cubic,and Au hexagonal close-packed regions are examined and detected by machine learning methods to demonstrate the potential of artificial intelligence for hyperspectral STS mapping.

关键词： autonomous consuming packed

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