A fast,robust pipeline for strain mapping of crystalline materials is important for many technological applications.Scanning electron nanodiffraction allows us to calculate strain maps with high accuracy and spatial r...A fast,robust pipeline for strain mapping of crystalline materials is important for many technological applications.Scanning electron nanodiffraction allows us to calculate strain maps with high accuracy and spatial resolutions,but this technique is limited when the electron beam undergoes multiple scattering.Deep-learning methods have the potential to invert these complex signals,but require a large number of training examples.We implement a Fourier space,complex-valued deep-neural network,FCU-Net,to invert highly nonlinear electron diffraction patterns into the corresponding quantitative structure factor images.FCU-Net was trained using over 200,000 unique simulated dynamical diffraction patterns from different combinations of crystal structures,orientations,thicknesses,and microscope parameters,which are augmented with experimental artifacts.We evaluated FCU-Net against simulated and experimental datasets,where it substantially outperforms conventional analysis methods.Our code,models,and training library are open-source and may be adapted to different diffraction measurement problems.展开更多
The authors became aware of a mistake in the original version of this Article.Specifically,some of the band gap values plotted and reported in Fig.1c and Table SI-1 were incorrect.This error originated because two dif...The authors became aware of a mistake in the original version of this Article.Specifically,some of the band gap values plotted and reported in Fig.1c and Table SI-1 were incorrect.This error originated because two different types of k-point meshes were used in DFT computations performed on CdTe,CdSe and CdS:one which is gamma-centered and one which is not gamma-centered.展开更多
The ability to predict the likelihood of impurity incorporation and their electronic energy levels in semiconductors is crucial for controlling its conductivity,and thus the semiconductor’s performance in solar cells...The ability to predict the likelihood of impurity incorporation and their electronic energy levels in semiconductors is crucial for controlling its conductivity,and thus the semiconductor’s performance in solar cells,photodiodes,and optoelectronics.The difficulty and expense of experimental and computational determination of impurity levels makes a data-driven machine learning approach appropriate.In this work,we show that a density functional theory-generated dataset of impurities in Cd-based chalcogenides CdTe,CdSe,and CdS can lead to accurate and generalizable predictive models of defect properties.展开更多
基金This work was primarily funded by the US Department of Energy in the program“4D Camera Distillery:From Massive Electron Microscopy Scattering Data to Useful Information with AI/ML.”M.K.Y.C.and C.O.each acknowledge support of a US Department of Energy Early Career Research Award+4 种基金J.C.acknowledges support from the Presidential Early Career Award for Scientists and Engineers(PECASE)through the U.S.Department of Energy.B.H.S.and py4DSTEM development are supported by the Toyota Research InstituteS.E.Z.was supported by the National Science Foundation under STROBE Grant no.DMR 1548924Work at the Molecular Foundry was supported by the Office of Science,Office of Basic Energy Sciences,of the US Department of Energy under Contract No.DE-AC02-05CH11231Use of the Center for Nanoscale Materials,an Office of Science user facility,was supported by the U.S.Department of Energy,Office of Science,Office of Basic Energy Sciences,under Contract No.DE-AC02-06CH11357This research used resources of the National Energy Research Scientific Computing Center,a DOE Office of Science User Facility supported by the Office of Science of the U.S.Department of Energy under Contract No.DE-AC02-05CH11231。
文摘A fast,robust pipeline for strain mapping of crystalline materials is important for many technological applications.Scanning electron nanodiffraction allows us to calculate strain maps with high accuracy and spatial resolutions,but this technique is limited when the electron beam undergoes multiple scattering.Deep-learning methods have the potential to invert these complex signals,but require a large number of training examples.We implement a Fourier space,complex-valued deep-neural network,FCU-Net,to invert highly nonlinear electron diffraction patterns into the corresponding quantitative structure factor images.FCU-Net was trained using over 200,000 unique simulated dynamical diffraction patterns from different combinations of crystal structures,orientations,thicknesses,and microscope parameters,which are augmented with experimental artifacts.We evaluated FCU-Net against simulated and experimental datasets,where it substantially outperforms conventional analysis methods.Our code,models,and training library are open-source and may be adapted to different diffraction measurement problems.
文摘The authors became aware of a mistake in the original version of this Article.Specifically,some of the band gap values plotted and reported in Fig.1c and Table SI-1 were incorrect.This error originated because two different types of k-point meshes were used in DFT computations performed on CdTe,CdSe and CdS:one which is gamma-centered and one which is not gamma-centered.
基金We acknowledge funding from the US Department of Energy SunShot program under contract DOE DEEE005956Use of the Center for Nanoscale Materials,an Office of Science user facility,was supported by the U.S.Department of Energy,Office of Science,Office of Basic Energy Sciences,under Contract No.DE-AC02-06CH11357+2 种基金This research used resources of the National Energy Research Scientific Computing Center,a DOE Office of Science User Facility supported by the Office of Science of the U.S.Department of Energy under Contract No.DE-AC02-05CH11231M.Y.T.would like to acknowledge support from the U.S.Department of Energy,Office of Science,Office of Workforce Development for Teachers and Scientists(WDTS)under the Science Undergraduate Laboratory Internship(SULI)programM.J.D.was was supported by the U.S.Department of Energy,Office of Basic Energy Sciences,Division of Chemical Sciences,Geosciences,and Biosciences,under Contract No.DE-AC02-06CH11357.
文摘The ability to predict the likelihood of impurity incorporation and their electronic energy levels in semiconductors is crucial for controlling its conductivity,and thus the semiconductor’s performance in solar cells,photodiodes,and optoelectronics.The difficulty and expense of experimental and computational determination of impurity levels makes a data-driven machine learning approach appropriate.In this work,we show that a density functional theory-generated dataset of impurities in Cd-based chalcogenides CdTe,CdSe,and CdS can lead to accurate and generalizable predictive models of defect properties.
