Supplementary code for the paper: https://arxiv.org/abs/1803.05381

In this project, we used a machine learning framework to identify, extract, cluster, analyze, and track defects in a scanning transmission electron microscopy (STEM) movie of 2D material (WS₂).

This repository contains 3 notebooks.

1. WS2_Defects_DeepLearning_gmm.ipynb is the main workflow, starting, involving:

   1.1) Generating traning set from the first frame of the image by utilizing Fast Fourier Transform subtraction (FFT-subtraction) to create labels.
   1.2) Training a fully convolutional neural network (FCNN) for defect identification.
   1.3) Extraction of defects from all frames using the trained FCNN.
   1.4) Clustering the extracted defects using a Gausian Mixture Model (GMM).
   1.5) Tracking the evolution of selected defects and calculating diffusion

2. WS2_Defects_LocalCryst_PCA.ipynb uses clustered defects to perfrom local crystallographic analysis using principal component analysis (PCA)

3. WS2_Defects_Markov_Transitions.ipynb uses extracted defect trajectories to calculate transition probabilities.