DeepDFT: Neural Message Passing Network for Accurate Charge Density Prediction
This work addresses the need for accurate and scalable charge density prediction in electronic structure simulations for molecules, solids, and liquids, representing an incremental improvement over existing methods.
The authors tackled the problem of predicting electronic charge density around atoms using a deep learning model called DeepDFT, achieving lower average prediction errors than variations observed in density functional theory simulations with different exchange-correlation functionals.
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.