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

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 

作者机构：Molecular FoundryLawrence Berkeley National LaboratoryBerkeleyCA94720United States Advanced Light SourceLawrence Berkeley National LaboratoryBerkeleyCA94720United States Department of Physics and NanoScience Technology CenterUniversity of Central FloridaOrlandoFL32816United States Department of Materials Science and EngineeringThe Pennsylvania State UniversityUniversity ParkPA16082United States Center for Two-Dimensional and Layered MaterialsThe Pennsylvania State UniversityUniversity ParkPA16802United States Department of Physics and Department of ChemistryThe Pennsylvania State UniversityUniversity ParkPA16802United States Center for Advanced Mathematics for Energy Research ApplicationsLawrence Berkeley National LaboratoryBerkeleyCA94720United States 

出 版 物：《npj Computational Materials》 (计算材料学（英文）)

年 卷 期：2022年第8卷第1期

页      面：916-922页

核心收录：

学科分类：08[工学] 0805[工学-材料科学与工程（可授工学、理学学位）] 080502[工学-材料学] 

基　　金：Work was performed at the Molecular Foundry supported by the Office of Science,Office of Basic Energy Sciences,of the U.S.Department of Energy under contract no.DE-AC02-05CH11231 Work was also funded through the Center for Advanced Mathematics for Energy Research Applications(CAMERA),which is jointly funded by the Advanced Scientific Computing Research(ASCR)and Basic Energy Sciences(BES)within the Department of Energy’s Office of Science,under Contract No.DE-AC02-05CH11231 S.K and J.A.R.acknowledge support from the National Science Foundation Division of Materials Research(NSF-DMR)under awards 2002651 and 2011839 L.F.acknowledges funding from the Swiss National Science Foundation(SNSF)via Early PostDoc Mobility Grant no.P2ELP2_184398 

主　　题：autonomous consuming packed 

摘      要：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.