nNPipe: A neural network pipeline for automated analysis of morphologically diverse catalyst systems - Resources

This dataset comprises of resources required to replicate the results described in "<em>nNPipe</em>: A neural network pipeline for automated analysis of morphologically diverse catalyst systems". <em>nNPipe </em>is a deep learning based method in which two deep convolutional neural networks are used for the automated analysis of 2048x2048 HRTEM images. The file contains:<br> - Relevant experimental images as well as ground truth for Pd/C and Au/Ge systems.<br> - A workflow file explaining the nNPipe workflow.<br> - Mathematica 12.1 code for the generation of computational models.<br> - MATLAB code for HRTEM multislice simulations using MULTEM, as well as code required to form respective training datasets.<br> - Weights and files required for training the YOLOv5x module.<br> - Weights and files required for training the SegNet module.<br> - Mathematica 12.1 code required for reconstruction of 2048x2048 binary segmented maps of HRTEM images.