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We introduce the use of Crystal Graph Convolutional Neural Networks (CGCNN),Fully Connected Neural Networks (FCNN) and XGBoost to predict thermoelectricproperties. The dataset for the CGCNN is independent of Density FunctionalTheory (DFT) and only relies on the crystal and atomic information, while thatfor 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 topredict the scattering-time independent thermoelectric powerfactor much betterthan the CGCNN (or XGBoost), suggesting that bonding and density of statesdescriptors informed from materials science knowledge obtained partially fromDFT are vital to predict functional properties.

Predicting thermoelectric properties from crystal graphs and material descriptors - first application for functional materials