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Model interpretability with CrabNet

Use these steps to explore model interpretability methods with CrabNet and to reproduce the results presented in the publication:

A. Y.-T. Wang, M. S. Mahmoud, M. Czasny, A. Gurlo, CrabNet for Explainable Deep Learning in Materials Science: Bridging the Gap Between Academia and Industry, Integr. Mater. Manuf. Innov., 2022, 11 (1): 41-56. DOI: 10.1007/s40192-021-00247-y.

Table of Contents

  • Notes about the generation of attention videos
  • Reproduction of publication results

Notes about the generation of attention videos

The generation of attention videos during training is done in a few steps:

  1. during training, the attention matrices are extracted from the model at each ministep / epoch (configurable)
  2. the matrices are stored serially in a Zarr array
  3. after training, the Zarr arrays are re-processed to reorganize the storage structure for the quick recall of specific chemical compositions
  4. the arrays are dynamically accessed and the attention matrices plotted using matplotlib
  5. the plotted matrices are encoded into videos using ffmpeg

These steps require a large amount of fast storage and GPU VRAM. In addition, having a high number of CPU cores and system RAM will be helpful. Alternatively, you can run the scripts on a high-performance computing cluster.

Reproduction of publication results

To reproduce the publication results, run these scripts in order:

  • get_training_attention.py: train CrabNet with different material property datasets and save Zarr arrays with the obtained attention values.
  • generate_attention_videos.py: take the saved Zarr arrays, plot the attention maps and progress plots, and merge these into attention videos using ffmpeg.
  • guess_oxidation.py: use Pymatgen to guess the oxidation of elements within the compounds in the material property datasets. Saved oxidation guesses are provided in the file oxidation.zip in the data directory.
  • get_crabnet_embeddings.py: save learned element embeddings from CrabNet/HotCrab. Saved embeddings are provided in the files embeddings_crabnet_mat2vec.zip and embeddings_crabnet_onehot.zip in the data directory.
  • plot_element_correlation.py: plot the Pearson correlation matrices between element vectors using different element representations (both static and interactive plots).
  • plot_local_edm_umap.py: get individual EDM representations of atoms from within compounds and plot them as a UMAP.
  • plot_global_edm_umap.py: get global EDM representations of compounds and plot them as a UMAP.
  • get_dataset_stats.py: get descriptive statistics of the datasets as well as compute and plot Shannon entropy of the datasets.