Data-Driven Electron Microscopy

Data-Driven Electron Microscopy for Energy and Quantum Materials Research

 

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J. Smith et. al., ACS Nano, 17 (12) 2023;  H. Ni et. al., Nano Lett. 23 (16) 2023; T. Blum et. al., Small Methods, 5(5)2021; J. Smith et. al., Small Methods, 2024. 

 

 

Selected publications:

  1. Smith, J., Tran, H., Roccapriore, K. M., Shen, Z., Zhang, G., & Chi, M. Advanced Compressive Sensing and Dynamic Sampling for 4D-STEM Imaging of Interfaces. Small Methods, 2400742. DOI: 10.1002/smtd.202400742. [Link]
  2. Smith, Jacob and Huang, Zhennan and Gao, Wenpei and Zhang, Guannan and Chi, Miaofang, Atomic Resolution Cryogenic 4D-STEM Imaging via Robust Distortion Correction, ACS Nano, 2023, DOI: 10.1021/acsnano.2c12777. [Link]
  3. Haoyang Ni, Zhenyao Wu, Xinyi Wu, Jacob G. Smith, Michael J. Zachman, Jian-min Zuo, Lili Ju, Guannan Zhang, Miaofang Chi, Quantifying Atomically Dispersed Catalysts Using Deep Learning Assisted Microscopy, Nano Letters, 2023, DOI: 10.1021/acs.nanolett.3c01892. [Link]
  4. Thomas Blum, Jeffery Graves, Michael J. Zachman, Felipe Polo-Garzon, Zili Wu, Ramakrishnan Kannan, Xiaoqing Pan, Miaofang Chi, Machine Learning Method Reveals Hidden Strong Metal-Support Interaction in Microscopy Datasets, Small Methods, 2021, DOI: 10.1002/smtd.202100035. [Link]