We introduce DeepDFT, a deep learning model for predicting the electronic
charge density around atoms, the fundamental variable in electronic structure
simulations from which all ground state properties can be calculated. The model
is formulated as neural message passing on a graph, consisting of interacting
atom vertices and special query point vertices for which the charge density is
predicted. The accuracy and scalability of the model are demonstrated for
molecules, solids and liquids. The trained model achieves lower average
prediction errors than the observed variations in charge density obtained from
density functional theory simulations using different exchange correlation
functionals.Comment: Workshop paper presented at Machine Learning for Molecules Workshop
at NeurIPS 2020. Implementation and pretrained model are available at
https://github.com/peterbjorgensen/DeepDF
