prediction-of-magnetic-properties-using-cgcnn's Introduction

Prediction-of-Magnetic-Properties-using-CGCNN

Requirements

This package requires:

Pytorch
scikit-learn
pymatgen

To prepare material data, there are some additional requirements:

requests
tqdm
click

How to train

Since it is impossible to upload all data to github, to reproduce the training the data set should be downloaded with prepare_data.py and used for training.

How to prepare data

First, you should get your own API from The Materials Project databse(https://materialsproject.org/). Then, please write it to the your-api-key.txt. To prepare material data,

python prepare_data.py --input=mp-ids-46744.csv --output=id_prop.csv

How to train

Before you train our model, you should prepare dataset.

python main.py --train-ratio 0.6 --val-ratio 0.2 --test-ratio 0.2 data/sample-regression

How to predict

We provide some good performance samples. Use prediction.py as a sample prediction To predict with our pre-trained model,

python predict.py dataset_manipulated_pretrained/model_best.pth.tar data/sample-regression

You will be able to see the prediction results at the generated file 'test_result.csv'

References

@article{PhysRevLett.120.145301,
  title = {Crystal Graph Convolutional Neural Networks for an Accurate and Interpretable Prediction of Material Properties},
  author = {Xie, Tian and Grossman, Jeffrey C.},
  journal = {Phys. Rev. Lett.},
  volume = {120},
  issue = {14},
  pages = {145301},
  numpages = {6},
  year = {2018},
  month = {Apr},
  publisher = {American Physical Society},
  doi = {10.1103/PhysRevLett.120.145301},
  url = {https://link.aps.org/doi/10.1103/PhysRevLett.120.145301}
}

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