Enhancing Access to Machine-Learning Models. We packaged our electronic classifiers and made them publically available. They are easily accessible via an interactive Jupyter notebook hosted by Binder.

Anyone can upload a structure file in CIF format and make their own prediction using the interactive Jupyter notebook. Since this notebook is hosted in a Docker containerized environment, any person interested in making a classification can execute the script immediately in their web browser without installing any dependencies.

This aspect greatly improves the usability of the code and broadens electronic materials research participation, especially to non-computational researchers and non-domain experts. The complete workflow behind the ML models is described in the project’s GitHub page with some sub-functions also demonstrated in an interactive Jupyter notebook.