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We introduce a computational framework to predict band offsets ofsemiconductor interfaces using density functional theory (DFT) and graph neuralnetworks (GNN). As a first step, we benchmark DFT based work function andelectron affinity values for surfaces against experimental data with accuraciesof 0.29 eV and 0.39 eV. Similarly, we evaluate band offset values usingindependent unit (IU) and alternate slab junction (ASJ) models leading toaccuracies of 0.45 eV and 0.22 eV respectively. During ASJ structuregeneration, we use Zur's algorithm along with a unified GNN force-field totackle the conformation challenges of interface design. At present, we have 300surface work functions calculated with DFT, from which we can compute 44850 IUband offsets as well as 250 directly calculated ASJ band offsets. Finally, asthe space of all possible heterojunctions is too large to simulate with DFT, wedevelop generalized GNN models to quickly predict band edges with an accuracyof 0.26 eV. We use such models to predict relevant quantities includingionization potentials, electron affinities, and IU-based band offsets. Weestablish simple rules using the above models to pre-screen potentialsemiconductor devices from a vast pool of nearly 1.4 trillion candidateinterfaces.

Accelerated Band Offset Prediction in Semiconductor Interfaces with DFT and Deep Learning