  Development of next-generation electronic devices for applications call for
the discovery of quantum materials hosting novel electronic, magnetic, and
topological properties. Traditional electronic structure methods require
expensive computation time and memory consumption, thus a fast and accurate
prediction model is desired with increasing importance. Representing the
interactions among atomic orbitals in any material, a material Hamiltonian
provides all the essential elements that control the structure-property
correlations in inorganic compounds. Effective learning of material Hamiltonian
by developing machine learning methodologies therefore offers a transformative
approach to accelerate the discovery and design of quantum materials. With this
motivation, we present and compare several different graph convolution networks
that are able to predict the band gap for inorganic materials. The models are
developed to incorporate two different features: the information of each
orbital itself and the interaction between each other. The information of each
orbital includes the name, relative coordinates with respect to the center of
super cell and the atom number, while the interaction between orbitals are
represented by the Hamiltonian matrix. The results show that our model can get
a promising prediction accuracy with cross-validation.
