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Abstract We report a deep learning‐based approach to accurately predict the emission spectra of phosphorescent heteroleptic [Ir(C6N)₂(N^N)]⁺complexes, enabling the rapid discovery of novel Ir(III) chromophores for diverse applications including organic light‐emitting diodes and solar fuel cells. The deep learning models utilize graph neural networks and other chemical features in architectures that reflect the inherent structure of the heteroleptic complexes, composed of C^N and N^N ligands, and are thus geared towards efficient training over the dataset. By leveraging experimental emission data, our models reliably predict the full emission spectra of these complexes across various emission profiles, surpassing the accuracy of conventional DFT and correlated wavefunction methods, while simultaneously achieving robustness to the presence of imperfect (noisy, low‐quality) training spectra. We showcase the potential applications for these and related models forin silicoprediction of complexes with tailored emission properties, as well as in “design of experiment” contexts to reduce the synthetic burden of high‐throughput screening. In the latter case, we demonstrate that the models allow us to exploit a limited amount of experimental data to explore a wide range of chemical space, thus leveraging a modest synthetic effort. 

Photochemical electrocyclization reactions are valued for both their ability to produce structurally complex molecules and their central role in elucidating fundamental mechanistic principles of photochemistry. We present herein a highly enantioselective 6π photoelectrocyclization catalyzed by a chiral Ir(III) photosensitizer. This transformation was successfully realized by engineering a strong hydrogen-bonding interaction between a pyrazole moiety on the catalyst and a basic imidazolyl ketone on the substrate. To shed light on the origin of stereoinduction, we conducted a comprehensive investigation combining experimental and computational mechanistic studies. Results from density functional theory calculations underscore the crucial role played by the prochirality and the torquoselectivity in the electrocyclization process as well as the steric demand in the subsequent [1,4]-H shift step. Our findings not only offer valuable guidance for developing chiral photocatalysts but also serve as a significant reference for achieving high levels of enantioselectivity in the 6π photoelectrocyclization reaction.