  We introduce the use of Crystal Graph Convolutional Neural Networks (CGCNN),
Fully Connected Neural Networks (FCNN) and XGBoost to predict thermoelectric
properties. The dataset for the CGCNN is independent of Density Functional
Theory (DFT) and only relies on the crystal and atomic information, while that
for the FCNN is based on a rich attribute list mined from Materialsproject.org.
The results show that the optimized FCNN is three layer deep and is able to
predict the scattering-time independent thermoelectric powerfactor much better
than the CGCNN (or XGBoost), suggesting that bonding and density of states
descriptors informed from materials science knowledge obtained partially from
DFT are vital to predict functional properties.
