OrbNet: Deep Learning for Quantum Chemistry Using Symmetry-Adapted Atomic-Orbital Features. (arXiv:2007.08026v3 [physics.chem-ph] UPDATED)

We introduce a machine learning method in which energy solutions from the
Schrodinger equation are predicted using symmetry adapted atomic orbitals
features and a graph neural-network architecture. \textsc{OrbNet} is shown to
outperform existing methods in terms of learning efficiency and transferability
for the prediction of density functional theory results while employing
low-cost features that are obtained from semi-empirical electronic structure
calculations. For applications to datasets of drug-like molecules, including
QM7b-T, QM9, GDB-13-T, DrugBank, and the conformer benchmark dataset of
Folmsbee …

arxiv chemistry deep learning learning physics