Unveiling the design rules for tunable emission in graphene quantum dots: A high-throughput TDDFT and machine learning perspective

The ability to tailor the optical properties of graphene quantum dots (GQDs) is critical for their application in optoelectronics, bioimaging and sensing. However, a comprehensive understanding of how shape, size and doping influence their emission properties remains elusive. In this study, we conduct a systematic high-throughput time-dependent density functional theory (TDDFT) and machine learning analysis of 284 distinct GQDs, varying in shape (square, hexagonal, amorphous), size (similar to\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\sim$$\end{document}1-2 nm) and doping configurations with elements B, N, O, S and P at varying concentrations (1.5-7%). Our findings reveal clear design principles for tuning emission wavelengths based on dopant type, concentration and GQD geometry. Notably, sulfur doping at specific concentrations consistently results in higher emission energies, with certain configurations yielding emissions within the visible range. By elucidating how quantum confinement effects, symmetry breaking and dopant-induced modifications govern GQD optical properties, we provide practical design rules for tailoring emission spectra for next-generation optoelectronic, bioimaging and sensing applications.