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Machine learning-driven analysis of atomic force microscopy (AFM) data for mixed-cation perovskite (PVSK) using variational autoencoders (VAEs).

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GongMLGroup/PVSK-MixedCation-AFM-VAE

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PVSK-MixedCation-AFM-VAE

This is a small collection of notebooks by Yongtao Liu and Jiawei Gong used to analyze atomic force microscopy (AFM) data for mixed-cation perovskite (PVSK) using variational autoencoders (VAEs).


Notebooks

  • ToJiawei_SciFiReader.ipynb (preview)
    • Test of the SciFiReader package for loading AFM images. (Gong)
  • PVSK_Topo_grainsampling_to_Jiawei_SciFiReader.ipynb (preview)
    • Detection of grains in AFM images for sampling in Machine Learning applications (Liu)
  • imspec2_v3a_IV_Jiawei.ipynb (preview)
    • Joint image/spec VAE with latent space 'alignment' (cosine loss + linear map) & rotation/translation-invariant image VAE for perovskite IV analysis (Liu & Gong).
  • PerovskiteDataset.ipynb (preview)
    • Visualization of Process Cycle Efficiency (PCE), open circuit voltage (Voc), short circuit current density (JSC) and fill factor (FF) from the Perovskite Database. (Liu)

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Machine learning-driven analysis of atomic force microscopy (AFM) data for mixed-cation perovskite (PVSK) using variational autoencoders (VAEs).

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