Insights into the Luminescence Quantum Yields of Cyclometalated Iridium(III) Complexes: A Density Functional Theory and Machine Learning Approach

Abstract

Cyclometalated iridium(III) complexes have been used in various optical materials, including organic light-emitting diodes (OLEDs) and photocatalysts, and a deeper understanding and prediction of their luminescence quantum yields (LQYs) greatly aid in accelerating material design. In this study, we integrated density functional theory (DFT) calculations with machine learning (ML) techniques to extract factors controlling LQY. Although a substantial data set of Ir(III) complexes and their LQYs is indispensable for constructing accurate ML models to predict LQYs, generating this type of data set is challenging due to the complexities associated with ab initio calculations of LQYs. To address this issue, we investigated the nonradiative decay process of nine Ir(III) complexes emitting blue to green, each exhibiting varying experimental LQYs, by using DFT calculations. For all nine complexes, the quenching process was induced by the rotation of the single bond in one of the ligands, which converted the six-coordinate structure to the five-coordinate structure. Since the decay mechanism was common for the nine Ir(III) complexes, parameters correlated with LQYs could be used as objective variables instead of LQYs. Based on this idea, we collected a data set featuring Ir(III) complexes and the energy differences between their six-and five-coordinate triplet structures, which correlated with LQYs. We also constructed ML models using the calculated LQYs as the objective variables with the parameters from the ground-state calculations as explanatory variables. The analyses of the constructed model revealed that the LUMO energy of the ligand made the most significant negative contribution to LQY. This suggests that the potential energy surface of the metal-to-ligand charge transfer (MLCT) excited state, which stabilizes the six-coordinate structure, is reduced by decreasing the energy of the unoccupied orbitals.

Machine Learning Prediction of Quantum Yields and Wavelengths of Aggregation-Induced Emission Molecules

“…The evaluation of the algorithms' performance included metrics such as the receiver operating characteristic curve (ROC), area under the curve (AUC), accuracy rate (ACC), and F1-score (F1). For wavelength predictions (λ abs , λ em_mono , λ em_agg ), four regression ML algorithms were selected: RF, KNN, GBR, and least absolute shrinkage and selection operator (LASSO) regression algorithms, which were all adopted in the prediction of wavelengths in recent reports [55,56]. Pearson correlation coefficient (r), mean relative error (MRE), and mean absolute error (MAE) were used to evaluate the algorithms' performances.…”