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

## TL;DR

This paper introduces CHIPS-TB, a framework to evaluate tight-binding models for predicting semiconductor material properties efficiently.

## Contribution

The novel contribution is a standardized framework and benchmarking protocol for tight-binding models across diverse materials.

## Key findings

- CHIPS-TB evaluates TB parametrizations for electronic bandgaps, band structures, and bulk modulus.
- The framework compares models like DFTB-based MatSci and PBC against DFT and experimental data for over 50 materials.
- Results highlight model performance differences relevant to semiconductor design.

## Abstract

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.