AbstractThe design of iridium(III) complex‐based photofunctional materials is challenged by commercial viability issues in organic light‐emitting diodes (OLEDs) and the limitation of polypyridyl structures for Ir(III) catalysts in photocatalysis. The burgeoning advancement of data‐driven science provides new solutions to this challenge. Herein, a three‐step data‐driven design approach is reported to target multifunctional red phosphorescent Ir(III) complexes. The approach uses machine learning and quantum chemistry calculations coupled with key excited‐state physical and chemical descriptors to identify the promising natural uracil‐based Ir(III) complexes from the self‐established “OLED Materials and Photocatalyst Database.” The newly designed red complexes demonstrate higher quantum yields and longer radiative lifetimes compared to their commercial counterparts. As photosensitizers, they display electron and energy transfer rates with substrates at the nanosecond level and are validated for lab‐scale photocatalysis. As the guests for OLEDs, their devices produce excellent red emission with low turn‐on voltages, minimal efficiency roll‐off, and decent electroluminescence efficiency. This research stands for an innovative endeavor in the data‐driven design of Ir(III) complexes, diverging from the traditional structure‐mimicking model, and can be generalized to the design of more photofunctional molecular materials.
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