We report a deep learning-based approach to accurately predict the emission spectra of phosphorescent heteroleptic [Ir(C^N)2(NN)]+ 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 for \insilico\ prediction 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 to exploit a limited amount of experimental data to explore a wide range of chemical space, thus leveraging a modest synthetic effort.
Keywords: emission spectrum, machine learning, artificial intelligence, graph neural network.
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