Machine learning of high-throughput DFT electron densities

Kohn-Sham density functional theory (DFT) provides a good balance between accuracy and efficiency, and its utility has given rise to high-throughput DFT databases including the Materials Project and the Open Quantum Materials Database. In this talk, we demonstrate how electron density datasets from these databases can be used to train machine learning models that complement and enhance the capabilities of DFT. We quantify the accuracy of neural networks that predict electron densities, and also report the trends observed in electronic structure-property relationships.