polymerlearn is the main component of the repository. This contains all of the driver code for PolymerGNN, explainability tools, and utilities for proprocessing data in the format provided.
polymerlearn
├── __init__.py
├── explain
│ ├── __init__.py
│ ├── custom_gcam.py
│ ├── explain_gnn.py
│ └── modified_gnns.py
├── models
│ └── gnn
│ ├── __init__.py
│ ├── iv.py
│ ├── iv_evidential.py
│ ├── joint.py
│ └── tg.py
└── utils
├── __init__.py
├── graph_prep.py
├── losses.py
├── train_evidential.py
├── train_graphs.py
├── uncertainty.py
└── xyz2mol.py
We recommend using Python >3.8 to run this code and setting up a virtual environment into which the packages will be installed. If you use Anaconda, you can set up all the requirements from the yml file:
conda env create --name your_env_name --file polymergnn_env.yml
This sets up all of the packages, and Owen has verified that it can be used on the ISAAC cluster.
For more general setup instructions for external packages, please reference conda-instructions.md.
To install the polymerlearn package, use the following command:
cd /path/to/PolymerGNN
pip install -e .
If using conda, ensure you have pip installed into the conda environment (conda install pip).
note: As of Mar 24, DONT USE the requirements.txt file. Will update this later. Leaving the below bit for use later:
This should also install all dependencies for the package. Most dependencies are not strict, but ensure that you're running PyG >2.0.0. Please see requirements.txt for a list of Python packages required to run our code.
To view a general API overview of the package, see the notebooks in the demo directory. These include demo's on predicting IV, Tg, and running the joint model. Examples of both cross validation and regular training are shown.
To modify model architecture, please see the models in the polymerlearn/models/gnn directory. This contains models to perform IV, Tg, and joint prediction, including some experimental models with which the team has worked with.
If you have any questions, please reach out to Owen (owen_queen@hms.harvard.edu).
If you want to cite our code or paper, please use the following BibTex entry:
@article{queen2023polymer,
title={Polymer graph neural networks for multitask property learning},
volume={9},
ISSN={2057-3960},
DOI={10.1038/s41524-023-01034-3},
number={11},
journal={npj Computational Materials},
publisher={Nature Publishing Group},
author={Queen, Owen and McCarver, Gavin A. and Thatigotla, Saitheeraj and Abolins, Brendan P. and Brown, Cameron L. and Maroulas, Vasileios and Vogiatzis, Konstantinos D.},
year={2023},
pages={1–10},
language={en}
}