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 "nNPipe: A neural network pipeline for automated analysis of morphologically diverse catalyst systems". nNPipe 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:
- Relevant experimental images as well as ground truth for Pd/C and Au/Ge systems.
- A workflow file explaining the nNPipe workflow.
- Mathematica 12.1 code for the generation of computational models.
- MATLAB code for HRTEM multislice simulations using MULTEM, as well as code required to form respective training datasets.
- Weights and files required for training the YOLOv5x module.
- Weights and files required for training the SegNet module.
- Mathematica 12.1 code required for reconstruction of 2048x2048 binary segmented maps of HRTEM images.