Crystal graph attention neural networks for materials prediction
The code requires the following external packages:
- torch 1.8.0+cu101
- torch-cluster 1.5.9
- torch-geometric 1.6.3
- torch-scatter 2.0.6
- torch-sparse 0.6.9
- torch-spline-conv 1.2.1
- torchaudio 0.8.0
- torchvision 0.9.0+cu101
- pytorch-lightning 1.2.4
- pymatgen 2022.0.5
- tqdm
- numpy
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.