Advancing band structure simulations of complex systems of C, Si and SiC: a machine learning driven density functional tight-binding approach

Abstract

We present a machine learning (ML) workflow for optimizing electronic band structures using density functional tight binding (DFTB) to replicate the results of costly hybrid functional calculations. The workflow is trained on carbon, silicon, and silicon carbide systems, encompassing bulk, slab, and defect geometries. Our method accurately reproduces hybrid functional results by applying a DFTB-ML scheme to train and predict the scaling parameters of two-center integrals and on-site energies, which is particularly accurate for electronic band structures near the Fermi energy. The DFTB-ML model demonstrates excellent scaling transferability, enabling training on smaller systems while maintaining hybrid functional-level accuracy when predicting larger systems. The high accuracy and adaptability of our model highlight its potential for precise band structure predictions across diverse chemical environments.