Spatiotemporal Learning of Microstructure Evolution

This project applies spatiotemporal ML techniques for learning the microstructrure formation in materials and enable computationally affordable evolution predictions.

Installation

Load the relevant modules:

module load 2022
module load PyTorch/1.12.0-foss-2022a-CUDA-11.7.0 torchvision/0.13.1-foss-2022a-CUDA-11.7.0
module load VTK/9.2.0.rc2-foss-2022a h5py/3.7.0-foss-2022a Pillow/9.1.1-GCCcore-11.3.0 OpenCV/4.6.0-foss-2022a-CUDA-11.7.0-contrib

And install the required packages:

pip install -r requirements.txt
python setup.py develop

Data

Microstructure datasets considered:

• Phase Field: https://archive.materialscloud.org/record/2022.156
• Monte Carlo Potts: https://doi.org/10.5281/zenodo.11209659

Credit

This implementation alters and re-uses models from the following sources:

• OpenSTL Benchmark: https://github.com/chengtan9907/OpenSTL/
• VT Unet: https://github.com/himashi92/VT-UNet