Towards accelerating the discovery of efficient iridium(iii) emitters using a novel database and machine learning based only on structural formulas

Abstract

Cyclometalated iridium(III) complexes are excellent emitters for organic light-emitting diodes (OLEDs), but the design of these compounds requires substantial cost and experimental efforts. In this work, we aimed at a simple and fast data-driven prediction of their luminescence properties based on the experimental data. To this end, we created a database (IrLumDB) that contains experimentally measured luminescence properties for over 1200 structurally diverse bis-cyclometalated iridium(III) complexes from 340 peer-reviewed articles. Based on the IrLumDB, we trained machine learning models, which require only simplified molecular input line entry system (SMILES) of ligands, to accurately predict the wavelength of emission maxima (λ_max) and photoluminescence quantum yield (PLQY) for the iridium phosphors. We also developed a PLQY classifier, which allows identification of highly efficient emitters (PLQY > 0.5). Furthermore, we validated the models for λ_max prediction on the set of 33 experimentally obtained luminescence spectra for newly synthesized and characterized Ir(III) complexes. Our methodology will serve as a straightforward approach for the prediction of the luminescence properties of large sets of bis-cyclometalated iridium(III) complexes, enabling identification of potentially efficient phosphors for subsequent synthesis. The properties prediction and the IrLumDB exploration are available at https://irlumdb.streamlit.app/.