This repository contains the software used in the paper Automated Image Analysis for Single-Atom Detection in Catalytic Materials by Transmission Electron Microscopy published in the Journal of the American Chemical Society. If you use the resources of this repository, please cite the reference work.

AC-STEM atom localization

Atom localization on AC-STEM (Aberration-Corrected Scanning Transmission Electron Microscopy) images.

Requirements

This code uses python 3.7.5 (defined by the .python-version file in case you using pyenv). Highly recommended to work using a virtual environment (virtualenv venv && source venv/bin/activate). Install requirements via:

pip install -r requirements.txt

Run

To replicate SAC_CNN results reported in our publication, use the following script:

/bin/bash scripts/dl_replicate_results.sh

This will run the original SAC_CNN model models/model_existing.ckpt.

Alternatively, to re-run the entire pipeline use the following command:

/bin/bash scripts/dl_train_evaluate.sh

This includes all stages:

1. Generate a crops dataset.
2. Training a SAC-CNN architecture.
3. Inference using the trained model.
4. Evaluate performance results.

Custom executions

It is possible to run SAC-CNN in your own data. To do so, use python commands with input arguments as follows:

PYTHONPATH=$PROJECTPATH python atoms_detection/dl_detection.py dataset/my_custom_dataset.csv

where dataset/my_custom_dataset.csv is a CSV file specifying to all images that will be used to run detection. All images must be in TIF format and must be included inside the data/tif_data folder. The CSV file should be formatted as follows:

Filename,Coords,Split
my_custom_image_1.tif,,test
my_custom_image_2.tif,,,test
my_custom_image_3.tif,,,test
my_custom_image_4.tif,,,test
...

Credits

• High Performance Artificial Intelligence (HPAI) group, Barcelona Supercomputing Center (BSC).
• Department of Chemistry and Applied Biosciences, ETH Zurich.
• School of Chemical and Process Engineering and School of Chemistry, University of Leeds.
• SuperSTEM Laboratory, SciTech Daresbury Campus.
• Department of Physics, University of York, Heslington.
• School of Chemical and Process Engineering and School of Physics, University of Leeds.
• Institute of Chemical Research of Catalonia and The Barcelona Institute of Science and Technology.