Deep neural networks (DNNs) have been used to successfully predict molecular properties calculated based on the Kohn--Sham density functional theory (KS-DFT). Although this prediction is fast and accurate, we believe that a DNN model for KS-DFT must not only predict the properties but also provide the electron density of a molecule. This letter presents the quantum deep field (QDF), which provides the electron density with an unsupervised but end-to-end physics-informed modeling by learning the atomization energy on a large-scale dataset. QDF performed well at atomization energy prediction, generated valid electron density, and demonstrated extrapolation. Our QDF implementation is available at https://github.com/masashitsubaki/QuantumDeepField_molecule.
@article{arxiv.2011.07923,
title = {Quantum deep field: data-driven wave function, electron density generation, and atomization energy prediction and extrapolation with machine learning},
author = {Masashi Tsubaki and Teruyasu Mizoguchi},
journal= {arXiv preprint arXiv:2011.07923},
year = {2020}
}