noah-hoffmann/cgat

Crystal graph attention neural networks for materials prediction

CGAT

Crystal graph attention neural networks for materials prediction

The code requires the following external packages:

• torch 1.10.0+cu111
• torch-cluster 1.5.9
• torch-geometric 2.0.3
• torch-scatter 2.0.9
• torch-sparse 0.6.12
• torch-spline-conv 1.2.1
• torchaudio 0.10.0
• torchvision 0.11.1
• pytorch-lightning 1.5.8
• pymatgen 2022.2.25
• tqdm
• numpy
• gpytorch 1.6.0

newer package versions might work.

Cleaner Code will be added soon

The dataset used in the work can be found at https://archive.materialscloud.org/record/2021.128. There are some slight changes as most aflow materials denoted as possible outliers in the hull were recalculated and some systems from the materials project were updated. For the non-mixed perovskite systems the distance to the hull was recalculated with this updated dataset.

Usage

The package can be installed by cloning the repository and running

pip install .

in the repository.

(If one wants to edit the source code installing with pip install -e . is advised.)

After installing one can make use of the following console scripts:

• train-CGAT to train a Crystal Graph Network,
• prepare to prepare trainings data for use with CGAT,
• train-GP to train Gaussian Processes.

(A full list of command line arguments can be found by running the command with -h.)