Accelerating the Discovery of Efficient Phosphorescent Iridium(III) Complex Emitters with Targeted Color Gamuts Through Interpretable Machine Learning Models and Virtual Screening

To meet the growing demands across various application fields, there is an urgent need to develop phosphorescent iridium(III) complex luminescent materials that combine high luminous efficiency with precise color gamut control. In this study, we integrate machine learning with quantum chemical calculations to establish high-precision, robust, and interpretable quantitative structure–property relationship (QSPR) models for predicting emission wavelength, photoluminescence quantum yield (PLQY), and radiative transition rate constant (Kr). Based on model analysis, we propose a novel molecular design strategy for efficient phosphorescent iridium(III) complex emitters. Using this strategy, we designed 203 candidate structures and identified over 100 high-potential blue iridium(III) complexes with excellent PLQY. This work not only provides a comprehensive understanding of the photoluminescent mechanism but also opens new avenues for optimizing phosphorescent iridium(III) complexes.为满足各应用领域日益增长的需求，亟需开发兼具高发光效率与精准色域调控能力的磷光铱(III)配合物发光材料。本研究将机器学习与量子化学计算相结合，构建了高精度、鲁棒性强且可解释的定量构效关系（quantitative structure–property relationship, QSPR）模型，用于预测发射波长、光致发光量子产率（photoluminescence quantum yield, PLQY）以及辐射跃迁速率常数（radiative transition rate constant, Kr）。基于模型分析，我们提出了一种面向高效磷光铱(III)配合物发光体的新型分子设计策略。依托该策略，我们设计了203种候选结构，并筛选出100余种具备优异光致发光量子产率的高潜力蓝色磷光铱(III)配合物。本研究不仅全面阐明了光致发光机制，同时为磷光铱(III)配合物的优化开辟了全新路径。