Unveiling the Design Rules for Tunable Emission in Graphene Quantum Dots: A High-Throughput TDDFT and Machine Learning Perspective

TL;DR

This study combines high-throughput TDDFT calculations and machine learning to uncover design principles for tunable emission in graphene quantum dots, aiding targeted synthesis for specific optical applications.

Contribution

It provides a comprehensive dataset and analysis of how shape, size, and doping influence GQD emission, offering new insights for designing tailored optoelectronic materials.

Findings

Identified 12 GQDs with visible-range emission wavelengths.

Systematic trends linking shape, size, and doping to emission energies.

Created a dataset for machine learning to predict GQD optical properties.

Abstract

We present time-dependent density functional theory (TDDFT) calculations of fluorescence emission energies for 284 distinct graphene quantum dots (GQDs) of varying shapes (square, hexagonal, and amorphous) and sizes ($\sim$1-2 nm). These GQDs are doped with one or two elements from B, N, O, S, and P at dopant percentages of 1.5%, 3%, 5%, and 7%. Our study systematically investigates the trends and patterns in emission energies as a function of shape, size, dopant type and dopant percentage. Twelve structures are identified to have emission wavelengths in the visible spectrum. The emission energies derived from our calculations can guide the formulation of specific GQD mixtures to achieve desired emission spectra within and beyond the visible range for industrial applications. Furthermore, the extensive dataset, including emission energies along with molecular structures generated in this work, creates a DFT dateset for further machine learning studies.