  The thickness of 2D materials not only plays a crucial role in determining
the performance of nanoelectronic and optoelectronic devices but also
introduces complexities in predicting volume-dependent properties such as
energy storage capacity, due to the intrinsic vacuum within these materials.
Although a plethora of experimental techniques, including but not limited to
optical contrast, Raman spectroscopy, nonlinear optical spectroscopy,
near-field optical imaging, and hyperspectral imaging, facilitate the
measurement of 2D material thickness, comprehensive data for many materials
remains elusive. Over the last decade, the exponential proliferation of 2D
materials and their heterostructures has outstripped the capabilities of
conventional experimental and computational approaches. In this evolving
landscape, machine learning (ML) has emerged as an indispensable tool, offering
novel avenues to augment these traditional methodologies. Addressing the
critical gap, we introduce THICK2D - Thickness Hierarchy Inference and
Calculation Kit for 2D Materials. This Python-based computational framework
harnesses an autogenerated thickness database, developed using large language
models (LLMs), and advanced ML algorithms to facilitate the rapid and scalable
estimation of material thickness, relying solely on crystallographic data. To
demonstrate the utility and robustness of THICK2D, we successfully employed the
toolkit to predict the thickness of more than 8000 2D-based materials, sourced
from two extensive 2D material databases. THICK2D is disseminated as an
open-source utility, accessible on GitHub https://github.com/gmp007/THICK2D,
and archived on Zenodo at
https://doi.org/10.5281/zenodo.11216648}{10.5281/zenodo.11216648.
