This software package implements our work of GATGNN-VOLTAGE for the problem voltage prediction. With GATGNN-VOLTAGE, one can predict the material's voltage from:
- a materials' formation-energy prediction alone or
- the reaction of a low and high potential energy materials.
Please read our paper for the detailed implementation of GATGNN-VOLTAGE:
Machine Learning and Evolution Laboratory
Department of Computer Science and Engineering
University of South Carolina
- Inside of your Python environemnt, install the basic dependencies required for GATGNN-VOLTAGE by running code below:
pip install -r requirements.txt- Follow the instructions listed on Pytorch-Geometric's documentations to install pytorch-geometric for using Graph Neural Network.
To obtain the dataset, run the get_data.py file.
python get_data.pyFor the reaction based voltage, run the voltage-reaction.py file. The 3 running options (evaluation, training, cross-validation or CV) can be set by using the --mode flag
- Details
for evaluating the performance of the trained reaction-model. Running this mode predicts voltage of electrodes from the testing-set and saves those results to RESULTS/voltage--prediction.csv.
- Usage example:
python voltage-reaction.py --mode evaluation- Details
for training a new reaction-based model.
- Optional arguments
| Parameter | Default | Description |
|---|---|---|
| --train_size | 0.8 | ratio size of the training-set |
| --batch | 128 | batch size to use within experinment |
| --graph_size | small | graph encoding format by neighborhood size, either 12 (small) or 16 (large) |
| --layers | 3 | number of AGAT layers to use in model (default:3) |
| --neurons | 64 | number of neurons to use per AGAT Layer |
| --heads | 4 | number of Attention-Heads to use per AGAT Layer |
- Usage example:
python voltage-reaction.py --mode training- Details
for running a k-fold cross-validation training/ evaluation method. Running this mode creates k different prediction-results which are saved to RESULTS/{k}-voltage--prediction.csv; where k corresponds to the cross-validation iteration.
- Optional arguments
| Parameter | Default | Description |
|---|---|---|
| --fold | 10 | number of folds |
| --train_size | 0.8 | ratio size of the training-set |
| --batch | 128 | batch size to use within experinment |
| --graph_size | small | graph encoding format by neighborhood size, either 12 (small) or 16 (large) |
| --layers | 3 | number of AGAT layers to use in model (default:3) |
| --neurons | 64 | number of neurons to use per AGAT Layer |
| --heads | 4 | number of Attention-Heads to use per AGAT Layer |
- Usage example:
python voltage-reaction.py --mode cross-validationLouis, S. Y., Siriwardane, E., Joshi, R., Omee, S., Kumar, N., & Hu, J. (2022). Accurate Prediction of Voltage of Battery Electrode Materials Using Attention Based Graph Neural Networks.
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Louis, S. Y., Zhao, Y., Nasiri, A., Wang, X., Song, Y., Liu, F., & Hu, J. (2020). Graph convolutional neural networks with global attention for improved materials property prediction. Physical Chemistry Chemical Physics, 22(32), 18141-18148.
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Omee, S. S., Louis, S. Y., Fu, N., Wei, L., Dey, S., Dong, R., ... & Hu, J. (2021). Scalable deeper graph neural networks for high-performance materials property prediction. arXiv preprint arXiv:2109.12283.
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Louis, S. Y., Nasiri, A., Rolland, F. J., Mitro, C., & Hu, J. (2021). NODE-SELECT: A Graph Neural Network Based On A Selective Propagation Technique. arXiv preprint arXiv:2102.08588.