Physics-Based AI-Augmented Workflows Discovering Candidate Graphene Quantum Dot Near-Infrared Chromophores

Graphene quantum dots (GQDs) exhibit optical properties that depend strongly on their size, edge structure, and surface functionalization, but identifying the molecular structures responsible for near-infrared (NIR) emission remains challenging because of their structural complexity and disorder. In this work, we developed a computational framework combining rule-based molecular generation, semi-empirical quantum chemistry calculations, machine learning, and time-dependent density functional theory (TD-DFT) to predict possible chemically realistic candidate GQDs with NIR-emitting optical properties. A fusion-based hexagonal lattice growth approach was used to generate 100,000 candidate graphitic structures. A random subset of 5000 structures was selected for semiempirical calculations. Computed band gaps were used to train and evaluate five machine learning models (Random Forest, XGBoost, LightGBM, CatBoost, and Neural Network). LightGBM predicted bandgaps were used to pre-screen the remaining 95,000 structures. 11,000 structures with low predicted gaps were selected for semiempirical calculations, and 50 top candidates with low gap and low SA score were selected for TD-DFT calculations employing LC-ωPBE functional. All 50 structures were predicted to exhibit NIR emission, offering a computed database of candidate NIR-emitting GQDs for future validation against experiment.