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| 1 | Manifold learning of four-dimensional scanning transmission electron microscopy显示文摘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. | Xin Li Ondrej E.Dyck Mark P.Oxley Andrew R.Lupini Leland McInnes John Healy Stephen Jesse Sergei V.Kalinin | 2019 | npj Computational Materials2019，，1： | 4 |
| 2 | Deep Bayesian local crystallography显示文摘The advent of high-resolution electron and scanning probe microscopy imaging has opened the floodgates for acquiring atomically resolved images of bulk materials,2D materials,and surfaces.This plethora of data contains an immense volume of information on materials structures,structural distortions,and physical functionalities.Harnessing this knowledge regarding local physical phenomena necessitates the development of the mathematical frameworks for extraction of relevant information.However,the analysis of atomically resolved images is often based on the adaptation of concepts from macroscopic physics,notably translational and point group symmetries and symmetry lowering phenomena.Here,we explore the bottom-up definition of structural units and symmetry in atomically resolved data using a Bayesian framework.We demonstrate the need for a Bayesian definition of symmetry using a simple toy model and demonstrate how this definition can be extended to the experimental data using deep learning networks in a Bayesian setting,namely rotationally invariant variational autoencoders. | Sergei V.Kalinin Mark P.Oxley Mani Valleti Junjie Zhang Raphael P.Hermann Hong Zheng Wenrui Zhang Gyula Eres Rama K.Vasudevan Maxim Ziatdinov | 2021 | npj Computational Materials2021，，1： | 3 |
| 3 | Probing atomic-scale symmetry breaking by rotationally invariant machine learning of multidimensional electron scattering显示文摘The 4D scanning transmission electron microscopy(STEM)method maps the structure and functionality of solids on the atomic scale,yielding information-rich data sets describing the interatomic electric and magnetic fields,structural and electronic order parameters,and other symmetry breaking distortions.A critical bottleneck is the dearth of analytical tools that can reduce complex 4D-STEM data to physically relevant descriptors.We propose an approach for the systematic exploration of 4D-STEM data using rotationally invariant variational autoencoders(rrVAE),which disentangle the general rotation of the object from other latent representations.The implementation of purely rotational rrVAE is discussed as are applications to simulated data for graphene and zincblende structures.The rrVAE analysis of experimental 4D-STEM data of defects in graphene is illustrated and compared to the classical center-of-mass analysis.This approach is universal for probing symmetry-breaking phenomena in complex systems and can be implemented for a broad range of diffraction methods. | Mark P.Oxley Maxim Ziatdinov Ondrej Dyck Andrew R.Lupini Rama Vasudevan Sergei V.Kalinin | 2021 | npj Computational Materials2021，，1： | 1 |
| 4 | Author Correction:Manifold learning of four-dimensional scanning transmission electron microscopy显示文摘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. | Xin Li Ondrej E.Dyck Mark P.Oxley Andrew R.Lupini Leland McInnes John Healy Stephen Jesse Sergei V.Kalinin | 2020 | npj Computational Materials2020，，1： | 0 |
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