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

We introduce a computational framework to predict band offsets of semiconductor interfaces using density functional theory (DFT) and graph neural networks (GNN). As a first step, we benchmark DFT based work function and electron affinity values for surfaces against experimental data with accuracies of 0.29 eV and 0.39 eV. 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. During ASJ structure generation, we use Zur's algorithm along with a unified GNN force-field to tackle the conformation challenges of interface design. At present, we have 300 surface work functions calculated with DFT, from which we can compute 44850 IU band offsets as well as 250 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 band edges with an accuracy of 0.26 eV. We use such models 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.