Individual atomic defects in 2D materials impact their macroscopic functionality.Correlating the interplay is challenging,however,intelligent hyperspectral scanning tunneling spectroscopy(STS)mapping provides a feasib...Individual atomic defects in 2D materials impact their macroscopic functionality.Correlating the interplay is challenging,however,intelligent hyperspectral scanning tunneling spectroscopy(STS)mapping provides a feasible solution to this technically difficult and time consuming problem.Here,dense spectroscopic volume is collected autonomously via Gaussian process regression,where convolutional neural networks are used in tandem for spectral identification.Acquired data enable defect segmentation,and a workflow is provided for machine-driven decision making during experimentation with capability for user customization.We provide a means towards autonomous experimentation for the benefit of both enhanced reproducibility and user-accessibility.Hyperspectral investigations on WS_(2)sulfur vacancy sites are explored,which is combined with local density of states confirmation on the Au{111}herringbone reconstruction.Chalcogen 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.展开更多
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-AC02-05CH11231Work 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+1 种基金S.K and J.A.R.acknowledge support from the National Science Foundation Division of Materials Research(NSF-DMR)under awards 2002651 and 2011839L.F.acknowledges funding from the Swiss National Science Foundation(SNSF)via Early PostDoc Mobility Grant no.P2ELP2_184398.
文摘Individual atomic defects in 2D materials impact their macroscopic functionality.Correlating the interplay is challenging,however,intelligent hyperspectral scanning tunneling spectroscopy(STS)mapping provides a feasible solution to this technically difficult and time consuming problem.Here,dense spectroscopic volume is collected autonomously via Gaussian process regression,where convolutional neural networks are used in tandem for spectral identification.Acquired data enable defect segmentation,and a workflow is provided for machine-driven decision making during experimentation with capability for user customization.We provide a means towards autonomous experimentation for the benefit of both enhanced reproducibility and user-accessibility.Hyperspectral investigations on WS_(2)sulfur vacancy sites are explored,which is combined with local density of states confirmation on the Au{111}herringbone reconstruction.Chalcogen 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.
