  Rechargeable Zn batteries with aqueous electrolytes have been considered as
promising alternative energy storage technology, with various advantages such
as low cost, high volumetric capacity, environmentally friendly, and high
safety. However, a lack of reliable cathode materials has largely pledged their
applications. Herein, we developed a machine learning (ML) based approach to
predict cathodes with high capacity (>150 mAh/g) and high voltage (>0.5V). We
screened over ~130,000 inorganic materials from the Materials Project database
and applied the crystal graph convolutional neural network (CGCNN) based ML
approach with data from the AFLOW database. The combination of these two could
not only screen cathode materials that match well with the experimental data
but also predict new promising candidates for further experimental validations.
We hope this study could spur further interests in ML-based advanced
theoretical tools for battery materials discovery.
