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

TL;DR

This paper introduces CHIPS-TB, a computational framework for evaluating and benchmarking tight-binding models across various materials, improving their transferability and reliability for semiconductor property predictions.

Contribution

We develop a standardized benchmarking framework (CHIPS-TB) for tight-binding models, enabling systematic comparison against DFT and experimental data across diverse materials.

Findings

CHIPS-TB effectively compares tight-binding parameterizations.

Benchmarking results highlight strengths and limitations of models.

Framework facilitates future model development and validation.

Abstract

As semiconductor technologies continue to scale down to the nanoscale, the efficient prediction of material properties becomes increasingly critical. The tight-binding (TB) method is a widely used semi-empirical 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 parameterizations across diverse material systems relevant to semiconductor design, focusing on properties such as electronic bandgaps, band structures, and bulk modulus. We assess model parameterizations 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.