Machine learning predicts electron densities with DFT accuracy

The need to use wavefunction or density functional theory (DFT) calculations to determine electron densities has been bypassed by a machine learning model. It will allow chemists to quickly determine properties that depend on the electron density of large systems such as van der Waals forces, halogen bonding and C-H–π interactions. These non-covalent interactions can hold insight into the binding of host–guest systems or favoured enantiomers within reaction pathways where intermediates and transition states may be stabilised by subtle attractions. The electron density distribution is one of the most powerful tools at the disposal of a computational chemist. From the electron density, properties such as charges, dipoles and electrostatic interaction energies can be determined.