The electronic evolution of Mott insulators into exotic correlated phases remains puzzling,because of electron interaction and inhomogeneity.Introduction of individual imperfections in Mott insulators could help captu...The electronic evolution of Mott insulators into exotic correlated phases remains puzzling,because of electron interaction and inhomogeneity.Introduction of individual imperfections in Mott insulators could help capture the main mechanism and serve as a basis to understand the evolution.Here we utilize scanning tunneling microscopy to probe the atomic scale electronic structure of the spin-orbit-coupling assisted Mott insulator Sr_(3)Ir_(2)O_(7).It is found that the tunneling spectra exhibit a homogeneous Mott gap in defect-free regions,but near the oxygen vacancy in the rotated Ir O_(2)plane the local Mott gap size is significantly enhanced.We attribute the enhanced gap to the locally reduced hopping integral between the 5d electrons of neighboring Ir sites via the bridging planar oxygen p orbitals.Such bridging defects have a dramatic influence on local bandwidth,thus provide a new way to manipulate the strength of Mottness in a Mott insulator.展开更多
We train a neural network to identify impurities in the experimental images obtained by the scanning tunneling microscope(STM)measurements.The neural network is first trained with a large number of simulated data and ...We train a neural network to identify impurities in the experimental images obtained by the scanning tunneling microscope(STM)measurements.The neural network is first trained with a large number of simulated data and then the trained neural network is applied to identify a set of experimental images taken at different voltages.We use the convolutional neural network to extract features from the images and also implement the attention mechanism to capture the correlations between images taken at different voltages.We note that the simulated data can capture the universal Friedel oscillation but cannot properly describe the non-universal physics short-range physics nearby an impurity,as well as noises in the experimental data.And we emphasize that the key of this approach is to properly deal with these differences between simulated data and experimental data.Here we show that even by including uncorrelated white noises in the simulated data,the performance of the neural network on experimental data can be significantly improved.To prevent the neural network from learning unphysical short-range physics,we also develop another method to evaluate the confidence of the neural network prediction on experimental data and to add this confidence measure into the loss function.We show that adding such an extra loss function can also improve the performance on experimental data.Our research can inspire future similar applications of machine learning on experimental data analysis.展开更多
基金the National Key R&D Program of China(Grant No.2017YFA0302900)the Basic Science Center Project of National Natural Science Foundation of China(Grant No.51788104)+4 种基金supported in part by the Beijing Advanced Innovation Center for Future Chip(ICFC)Open Research Fund Program of the State Key Laboratory of Low-Dimensional Quantum Physicssupported by the National Natural Science Foundation of China(Grant No.12074424)the Fundamental Research Funds for the Central Universitiesthe Research Funds of Renmin University of China。
文摘The electronic evolution of Mott insulators into exotic correlated phases remains puzzling,because of electron interaction and inhomogeneity.Introduction of individual imperfections in Mott insulators could help capture the main mechanism and serve as a basis to understand the evolution.Here we utilize scanning tunneling microscopy to probe the atomic scale electronic structure of the spin-orbit-coupling assisted Mott insulator Sr_(3)Ir_(2)O_(7).It is found that the tunneling spectra exhibit a homogeneous Mott gap in defect-free regions,but near the oxygen vacancy in the rotated Ir O_(2)plane the local Mott gap size is significantly enhanced.We attribute the enhanced gap to the locally reduced hopping integral between the 5d electrons of neighboring Ir sites via the bridging planar oxygen p orbitals.Such bridging defects have a dramatic influence on local bandwidth,thus provide a new way to manipulate the strength of Mottness in a Mott insulator.
基金supported by Beijing Outstanding Scholar Programthe National Key Research and Development Program of China(Grant No. 2016YFA0301600)+3 种基金the National Natural Science Foundation of China(Grant No. 11734010)supported by a startup fund from UCSDsupported by the Fundamental Research Funds for the Central Universitiesthe Research Funds of Renmin University of China
文摘We train a neural network to identify impurities in the experimental images obtained by the scanning tunneling microscope(STM)measurements.The neural network is first trained with a large number of simulated data and then the trained neural network is applied to identify a set of experimental images taken at different voltages.We use the convolutional neural network to extract features from the images and also implement the attention mechanism to capture the correlations between images taken at different voltages.We note that the simulated data can capture the universal Friedel oscillation but cannot properly describe the non-universal physics short-range physics nearby an impurity,as well as noises in the experimental data.And we emphasize that the key of this approach is to properly deal with these differences between simulated data and experimental data.Here we show that even by including uncorrelated white noises in the simulated data,the performance of the neural network on experimental data can be significantly improved.To prevent the neural network from learning unphysical short-range physics,we also develop another method to evaluate the confidence of the neural network prediction on experimental data and to add this confidence measure into the loss function.We show that adding such an extra loss function can also improve the performance on experimental data.Our research can inspire future similar applications of machine learning on experimental data analysis.
