Compositionally-Restricted Attention-Based Network (CrabNet)

The Compositionally-Restricted Attention-Based Network (CrabNet), inspired by natural language processing transformers, uses compositional information to predict material properties.

<img src=https://user-images.githubusercontent.com/45469701/155030619-3a5f75e8-b28d-4801-a54c-58a800ee874c.png width=150>

:warning: This is a fork of the original CrabNet repository :warning:

This is a refactored version of CrabNet, published to PyPI (pip) and Anaconda (conda). In addition to using .csv files, it allows direct passing of Pandas DataFrames as training and validation datasets, similar to automatminer. It also exposes many of the model parameters at the top-level via CrabNet and uses the sklearn-like "instantiate, fit, predict" workflow. An extend_features is implemented which allows utilization of data other than the elemental compositions (e.g. state variables such as temperature or applied load). These changes make CrabNet portable, extensible, and more broadly applicable, and will be incorporated into the parent repository at a later date. Please refer to the CrabNet documentation for details on installation and usage. If you find CrabNet useful, please consider citing the following publication in npj Computational Materials:

Citing

@article{Wang2021crabnet,
 author = {Wang, Anthony Yu-Tung and Kauwe, Steven K. and Murdock, Ryan J. and Sparks, Taylor D.},
 year = {2021},
 title = {Compositionally restricted attention-based network for materials property predictions},
 pages = {77},
 volume = {7},
 number = {1},
 doi = {10.1038/s41524-021-00545-1},
 publisher = {{Nature Publishing Group}},
 shortjournal = {npj Comput. Mater.},
 journal = {npj Computational Materials}
}