AI-Driven Accelerated Discovery of High-Performance Perovskite Quantum Dots Via Predictive LightGBM Modeling

The precise synthesis of stable, highly emissive all-inorganic perovskite quantum dots (QDs), particularly for red and blue emission, remains a formidable challenge due to complex phase transitions and extensive synthesis parameter spaces. Traditional empirical methods necessitate exhaustive trial-and-error experimentation, hindering rapid optimization. Here, we introduce an end-to-end artificial intelligence (AI)-driven framework based on a LightGBM model, trained on a curated data set of 406 experimental hot-injection synthesis protocols, to predict optimal synthesis conditions for achieving target photoluminescence (PL) characteristics. Through a strategic reduction from 30 million model-generated synthesis candidates to a select few experimentally validated conditions, we successfully synthesized blue-emitting CsPbX3 QDs (PL peak ∼ 465 nm, fwhm <19 nm) and red-emitting QDs (PL peak ∼ 630 nm, fwhm <30 nm) with significantly enhanced spectral purity and narrow emission line widths. Our model notably identifies critical synthesis parameters, including reaction temperature, precursor ratios, and ligand concentrations, effectively accelerating the experimental discovery timeline from weeks to less than a day. This study establishes a robust methodology for the rational, AI-guided design of perovskite materials, providing a generalizable platform to expedite future innovations in optoelectronic devices.