Machine Learned H\"uckel Theory: Interfacing Physics and Deep Neural Networks

Understand

The H\"uckel Hamiltonian is an incredibly simple tight-binding model famed for its ability to capture qualitative physics phenomena arising from electron interactions in molecules and materials.

• Part of its simplicity arises from using only two types of empirically fit physics-motivated parameters: the first describes the orbital energies on each atom and the second describes electronic interactions and bonding between atoms.
• By replacing these traditionally static parameters with dynamically predicted values, we vastly increase the accuracy of the extended H\"uckel model.
• The dynamic values are generated with a deep neural network, which is trained to reproduce orbital energies and densities derived from density functional theory.