GrainGraphNN

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graph neural networks for grain growth simulations

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# GrainGNN: A dynamic heterogeneous graph neural network for large-scale 3D grain microstructure evolution.
GrainGNN is a GNN surrogate model for predicting grain microstructure under additive manufacturing conditions. Paper: https://www.sciencedirect.com/science/article/pii/S0021999124003103
## Cite

If you are using the codes in this repository, please cite the paper
```
@article{qin2024graingnn,
  title={GrainGNN: A dynamic graph neural network for predicting 3D grain microstructure},
  author={Qin, Yigong and DeWitt, Stephen and Radhakrishnan, Balasubramaniam and Biros, George},
  journal={Journal of Computational Physics},
  volume={510},
  pages={113061},
  year={2024},
  publisher={Elsevier}
}
```

## Build
GrainGNN runs on python environment with versions python=3.7 and torch=1.12. Note that gcc compiler may be required for building torch-cluster etc.

```sh
conda create -n GNN python=3.7  
conda activate GNN
pip3 install torch==1.12.0+cu102 --extra-index-url https://download.pytorch.org/whl/cu102
pip3 install torch-geometric==2.1.0 torch-cluster==1.6.0 torch-scatter==2.1.0 torch-sparse==0.6.15 -f https://data.pyg.org/whl/torch-1.12.0+cu102.html
pip3 install -r requirements.txt   # other dependencies 
conda install -c conda-forge mayavi   # visualization
```
cu102 can be replaced by cpu or other CUDA versions depending on the platform

## Reproduce paper results
The trained models, including the regressor and classifier, are provided in the folder `model/`   
In the paper, the trained models are validated by comparing the predicted 3D field data with the high-fidelity data. We choose a `phase field (PF)` solver as the method to generate high-fidelity data. The codes can be found here https://github.com/YigongQin/cuPF. Under the folder `rawdat_PF/`, we provide two compressed PF data. One is of domain size (40um, 40um, 50um), and the other is of domain size (120um, 120um, 50um). To compare the GrainGNN results and PF results, and reproduce the results reported in the paper, follow the steps below.

### Steps
For the domain size (40um, 40um, 50um):
1. **Decompress the phase field data to .h5 file**
   ```sh
   gzip -d rawdat_PF/40_40/Epita_grains118_nodes236_frames120_G1.904_Rmax0.558_seed10020_Mt36920_train0_test1_rank0.h5.gz
   ```
2. **Extract phase field data and create the graph for t=0**
   ```sh
   # specify the seed number, domain size with `--lxd`, raw data folder, and the output folder for graphs
   python3 graph_trajectory.py --mode=test --rawdat_dir=./rawdat_PF/40_40/ --seed=10020 --lxd=40 --save_dir=./graphs/40_40/
   ```
   After this step, you should get the files `seed10020_G1.904_R0.558_span6.pkl` and `traj10020.pkl` in `graphs/40_40/`. You can verify them with the files given in the repo.
3. **Evolve the graph and get image results**
   
   ```sh
   # specify the folder of the graph data (--truth_dir) and seed number
   python3 test.py --truth_dir=./graphs/40_40/ --seed=10020
   ```
   After this step, you should get the plots for quantities of interest (QoIs) and image comparison for the last layer of grain structure. Compare the generated images with the `Fig.8` of the paper
![Alt text](https://raw.githubusercontent.com/YigongQin/GrainGraphNN/main/figures/paper_fig8.png)

Similarly for the domain of (120um, 120um, 50um):
```sh
gzip -d rawdat_PF/120_120/Epita_grains1043_nodes2086_frames120_lxd120.000008_G10.000_Rmax2.000_UC20.000_seed0_Mt10300_rank0.h5.gz
python3 graph_trajectory.py --mode=test --rawdat_dir=./rawdat_PF/120_120/ --seed=0 --lxd=120 --save_dir=./graphs/120_120/
python3 test.py --truth_dir=./graphs/120_120/ --seed=0
```
Results correspond to `Fig.11(a)` of the paper
![Alt text](https://raw.githubusercontent.com/YigongQin/GrainGraphNN/main/figures/paper_fig11.png)
### Numbers to check

| case | error of last layer | grain event accuracy |
|----------|----------|----------|
| /graphs/40_40/seed10020* | 0.11 | 72/75|
| /graphs/120_120/seed0* | 0.18 | 644/704 |

## Inference the models only
The accuracy of the model under various thermal and grain configurations is discussed in the paper. If you are interested in the same material -- `stainless steel 316L`, and a similar range of thermal conditions, you can choose to run GrainGNN inference without having the high-fidelity data:
```sh
# specify thermal parameters (`G,R`), domain size `lxd`, and a seed number
python3 graph_trajectory.py --mode=generate --seed=1 --lxd=40 --save_dir=./graphs/40_40/ --G=10 --R=2
python3 test.py --truth_dir=./graphs/40_40/ --seed=1 --no-compare
```
Explore more arguments in `graph_trajectory.py` for more configuration, such as changing the grain size and orientation distributions with `--size, --orien`

## Train models with the same phase field data (and extracted graphs) used in the paper
The graph pairs used for training can be downloaded here: https://drive.google.com/file/d/19uNh7XwHWMhF4PGLFcD-VM5YvYXBpHV_/view?usp=sharing  
Decompress the file and optionally change the training parameters in the `parameters.py`
```sh
gzip dataset_train.pkl.gz
python3 train.py --model_type=regressor --model_id=0 --device=cuda
python3 train.py --model_type=classifier --model_id=1 --device=cuda
```
Log files of the training process are provided in `model/` folder. The training curves can be found in the paper.

## Create a new training dataset
The size of the full phase field training data is very large. We are figuring out ways to share the raw phase field data. Try generating new phase field data using codes here https://github.com/YigongQin/cuPF    
To extract graph pairs from a PF `.h5` file for training:
 ```sh
 python3 graph_trajectory.py --mode=train --rawdat_dir=./rawdat_PF/40_40/ --seed=10020 --lxd=40 --save_dir=./graphs/40_40/
 ```
Then use `create_datasets.py` to combine graphs extracted from different PF files.

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