This repository provides the implementation of the Virtual Node Graph Neural Network (VGNN) for full phonon prediction in materials science. VGNN is designed to address the challenges in phonon prediction using graph neural networks.

1. Environment Setup
2. Tutorial
3. Using Pre-trained Models
4. Training the Model
5. Publications
6. References
7. Data Availability

To set up the environment locally and run the code:

1. Clone the repository:

   git clone https://github.com/RyotaroOKabe/phonon_prediction.git
   cd phonon_prediction

2. Create a virtual environment:

   conda create -n phonon python=3.9
   conda activate phonon

3. Install the required dependencies:

   pip install -r requirements.txt -f https://pytorch-geometric.com/whl/torch-${TORCH}+${CUDA}.html

   Replace ${TORCH} and ${CUDA} with your specific versions (e.g., cpu, cu118 for CUDA 11.8, and 2.0.0 for PyTorch 2.0). For example:

   pip install -r requirements.txt -f https://pytorch-geometric.com/whl/torch-2.0.0+cu118.html

4. Start Jupyter Notebook and open the notebooks:

   jupyter notebook

You can follow the tutorial by running the provided Jupyter notebooks:

• tutorial_VVN.ipynb
• tutorial_MVN.ipynb
• tutorial_kMVN.ipynb

To use a trained model from scratch, specify the pretrained_name in the code.

Note: Tutorial codes from a previous version are stored in the ./previous_codes/ folder. To run them, move the files to the parent directory and use tutorial_XXX_previous.ipynb.

To run phonon predictions on your own materials using the pre-trained models, use these notebooks:

• cif_VVN.ipynb
• cif_MVN.ipynb
• cif_kMVN.ipynb

Store your CIF files in the ./cif_folder/. The Jupyter notebooks will load the materials from this folder and perform the prediction. Seekpath automatically suggest the high-symmetry path for kMVN. Use the idx_out variable to specify which material to plot the results for.

To train the models from scratch, use the following commands:

• Train the VVN model:

   python train_vvn.py

• Train the MVN model for Gamma phonon prediction:

   python train_mvn.py

• Train the k-MVN model for phonon band structure prediction:

   python train_kmvn.py

• Virtual Node Graph Neural Network for Full Phonon Prediction
  Nature Computational Science (2024)
  Read the full paper

• Research Briefing: Virtual Node Graph Neural Network for Full Phonon Prediction
  Nature Computational Science (2024)
  Read the research briefing

• AI method radically speeds predictions of materials’ thermal properties
  Read the MIT News article

If you find this code or dataset useful, please cite the following paper:

@article{okabe2024virtual,
  title={Virtual node graph neural network for full phonon prediction},
  author={Okabe, Ryotaro and Chotrattanapituk, Abhijatmedhi and Boonkird, Artittaya and Andrejevic, Nina and Fu, Xiang and Jaakkola, Tommi S and Song, Qichen and Nguyen, Thanh and Drucker, Nathan and Mu, Sai and others},
  journal={Nature Computational Science},
  pages={1--10},
  year={2024},
  publisher={Nature Publishing Group US New York}
}

Architecture: Zhantao Chen, Nina Andrejevic, et al. "Virtual Node Graph Neural Network for Full Phonon Prediction." Adv. Sci. 8, 2004214 (2021). https://onlinelibrary.wiley.com/doi/10.1002/advs.202004214.

E(3)NN: Mario Geiger, Tess Smidt, Alby M., Benjamin Kurt Miller, et al. Euclidean neural networks: e3nn (2020) v0.4.2. https://doi.org/10.5281/zenodo.5292912.

seekpath: Y. Hinuma, G. Pizzi, Y. Kumagai, F. Oba, I. Tanaka, Band structure diagram paths based on crystallography, Comp. Mat. Sci. 128, 140 (2017) https://seekpath.readthedocs.io/en/latest/index.html.

Dataset: Guido Petretto, Shyam Dwaraknath, Henrique P. C. Miranda, Donald Winston, et al. "High-throughput Density-Functional Perturbation Theory phonons for inorganic materials." (2018) figshare. Collection. https://doi.org/10.6084/m9.figshare.c.3938023.v1

The data that support the findings of this study are openly available in GitHub at https://github.com/RyotaroOKabe/phonon_prediction. The $\Gamma$-phonon database generated with the MVN method is available at https://osf.io/k5utb/