CHIPS-TB: Evaluating Tight-Binding Models for Metals, Semiconductors, and Insulators

As semiconductor technologies continue to scale to the nanoscale, the efficient prediction of material properties becomes increasingly critical. The tight-binding (TB) method is a widely used semiempirical approach that offers a computationally tractable alternative to Density Functional Theory (DFT) for large-scale electronic structure calculations. However, conventional TB models often suffer from limited transferability and lack standardized benchmarking protocols. In this study, we introduce a computational framework (CHIPS-TB) for evaluating and comparing tight-binding parametrizations across diverse material systems relevant to semiconductor design, focusing on properties such as electronic bandgaps, band structures, and bulk modulus. We assess model parametrizations including Density Functional Tight-Binding (DFTB)-based MatSci, PBC, PTBP, SlaKoNet, and TB3PY against OptB88vdW, TBmBJ-DFT, and experimental reference data from the JARVIS-DFT database for 50+ materials pertinent to semiconductor applications. The CHIPS-TB code will be made publicly available on GitHub, and benchmarks will be available on JARVIS-Leaderboard.