  We introduce a computational framework (InterMat) to predict band offsets of
semiconductor interfaces using density functional theory (DFT) and graph neural
networks (GNN). As a first step, we benchmark OptB88vdW generalized gradient
approximation (GGA) work functions and electron affinities for surfaces against
experimental data with accuracies of 0.29 eV and 0.39 eV, respectively.
Similarly, we evaluate band offset values using independent unit (IU) and
alternate slab junction (ASJ) models leading to accuracies of 0.45 eV and 0.22
eV, respectively. We use bulk band structure calculations with the TBmBJ
meta-GGA functional to correct for band gap underestimation when predicting
conduction band properties. During ASJ structure generation, we use Zur
algorithm along with a unified GNN force-field to tackle the conformation
challenges of interface design. At present, we have 607 surface work functions
calculated with DFT, from which we can compute 183921 IU band offsets as well
as 593 directly calculated ASJ band offsets. Finally, as the space of all
possible heterojunctions is too large to simulate with DFT, we develop
generalized GNN models to quickly predict bulk band edges with an accuracy of
0.26 eV. We show how these models can be used to predict relevant quantities
including ionization potentials, electron affinities, and IU-based band
offsets. We establish simple rules using the above models to pre-screen
potential semiconductor devices from a vast pool of nearly 1.4 trillion
candidate interfaces. InterMat is available at website:
https://github.com/usnistgov/intermat
