Quantum deep field: data-driven wave function, electron density generation, and atomization energy prediction and extrapolation with machine learning
This work addresses the need for fast and accurate data-driven models in quantum chemistry that can provide electron densities, which is incremental as it builds on existing deep learning approaches for molecular property prediction.
The authors tackled the problem of predicting molecular properties and generating electron densities using deep neural networks, achieving accurate atomization energy prediction and valid electron density generation with demonstrated extrapolation capabilities.
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.