A Jupyter Notebook Repository for the paper "Predicting Critical Temperature of Doped and Alloyed Superconductors with Graph Convolutional Neural Networks".
(To be added soon)
To run this project with Python3, you will need to install the required dependencies. It is recommended that you do this inside of a Python3 virtual environment. To install dependencies, navigate to this directory and run:
pip3 install -r ./requirements.txt
To start a Jupyter notebook server in the jupyter subdirectory, run jupyter-notebook ./jupyter.
The most reliable way to run this project and view the associated notebooks is with Docker, an application containerization platform. If you have Docker installed on your system, you can build this project's Docker image by running:
docker build --no-cache -t sc-gnn:latest .
This will create an image called sc-gnn, which you can see by running docker images.
To start the image with an interactive terminal, run the bash script start_docker.sh in this directory. Alternatively, if you want to start the docker image with a Jupyter notebook server, run the bash script start_docker_jupyter.sh.
[1] Choudhary, K., DeCost, B.: Atomistic Line Graph Neural Network for improved materials property predictions. npj Comput Mater 7(1), 1–8 (Nov 2021). https://doi.org/10.1038/s41524-021-00650-1