Four-dimensional scanning transmission electron microscopy(4D-STEM)of local atomic diffraction patterns is emerging as a powerful technique for probing intricate details of atomic structure and atomic electric fields....Four-dimensional scanning transmission electron microscopy(4D-STEM)of local atomic diffraction patterns is emerging as a powerful technique for probing intricate details of atomic structure and atomic electric fields.However,efficient processing and interpretation of large volumes of data remain challenging,especially for two-dimensional or light materials because the diffraction signal recorded on the pixelated arrays is weak.Here we employ data-driven manifold leaning approaches for straightforward visualization and exploration analysis of 4D-STEM datasets,distilling real-space neighboring effects on atomically resolved deflection patterns from single-layer graphene,with single dopant atoms,as recorded on a pixelated detector.These extracted patterns relate to both individual atom sites and sublattice structures,effectively discriminating single dopant anomalies via multimode views.We believe manifold learning analysis will accelerate physics discoveries coupled between data-rich imaging mechanisms and materials such as ferroelectric,topological spin,and van der Waals heterostructures.展开更多
The original version of the published Article had a mistake in the Acknowledgements section.The Acknowledgments have been updated to the following:This research was supported by the US Department of Energy,Basic Energ...The original version of the published Article had a mistake in the Acknowledgements section.The Acknowledgments have been updated to the following:This research was supported by the US Department of Energy,Basic Energy Sciences,Materials Sciences and Engineering Division(M.P.O.,A.R.L.,S.V.K.)and conducted at the Center for Nanophase Materials Sciences,which is a US DOE Office of Science User Facility(X.L.,O.E.D.,S.J.).L.M.and J.H.acknowledge support from Tutte Institute for Mathematics and Computing,Canada.展开更多
基金This researdh was supported by the US Department of Energy,Basic Energy Sciences,Materials Sciences and Engineering Division(M.P.O,A.R.L,S.V.K)conducted at the Center for Nanophase Materials Sciences,which is a US DOE Office of Science User Facility(X.L,O.E.D.,SJ)L.M.and J.H.acknowledge support from Tutte Institute for Mathematics and Computing,Canada.
文摘Four-dimensional scanning transmission electron microscopy(4D-STEM)of local atomic diffraction patterns is emerging as a powerful technique for probing intricate details of atomic structure and atomic electric fields.However,efficient processing and interpretation of large volumes of data remain challenging,especially for two-dimensional or light materials because the diffraction signal recorded on the pixelated arrays is weak.Here we employ data-driven manifold leaning approaches for straightforward visualization and exploration analysis of 4D-STEM datasets,distilling real-space neighboring effects on atomically resolved deflection patterns from single-layer graphene,with single dopant atoms,as recorded on a pixelated detector.These extracted patterns relate to both individual atom sites and sublattice structures,effectively discriminating single dopant anomalies via multimode views.We believe manifold learning analysis will accelerate physics discoveries coupled between data-rich imaging mechanisms and materials such as ferroelectric,topological spin,and van der Waals heterostructures.
文摘The original version of the published Article had a mistake in the Acknowledgements section.The Acknowledgments have been updated to the following:This research was supported by the US Department of Energy,Basic Energy Sciences,Materials Sciences and Engineering Division(M.P.O.,A.R.L.,S.V.K.)and conducted at the Center for Nanophase Materials Sciences,which is a US DOE Office of Science User Facility(X.L.,O.E.D.,S.J.).L.M.and J.H.acknowledge support from Tutte Institute for Mathematics and Computing,Canada.
