Authors: Yudhajit Pal | Tahoe A. Fiala | Wesley B. Swords | Tehshik P. Yoon | J. R. Schmidt
Source: ChemPhysChem (article, 2024-05-16)
Field: Materials Science — Materials Chemistry

Predicting Emission Spectra of Heteroleptic Iridium Complexes Using Artificial Chemical Intelligence We report a deep learning-based approach to accurately predict the emission spectra of phosphorescent heteroleptic [Ir( <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:semantics> <mml:mrow> <mml:msup><mml:mrow><mml:mi>C</mml:mi></mml:mrow> <mml:mo>∧</mml:mo></mml:msup> <mml:mi>N</mml:mi></mml:mrow> <mml:annotation>${{\rm{C}}^\wedge {\rm{N}}}$</mml:annotation> </mml:semantics> </mml:math> )<sub>2</sub>( <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:semantics> <mml:mrow> <mml:msup><mml:mrow><mml:mi>N</mml:mi></mml:mrow> <mml:mo>∧</mml:mo></mml:msup> <mml:mi>N</mml:mi></mml:mrow> <mml:annotation>${{\rm{N}}^\wedge {\rm{N}}}$</mml:annotation> </mml:semantics> </mml:math> )]<sup>+</sup> 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 <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:semantics> <mml:mrow> <mml:msup><mml:mrow><mml:mi>C</mml:mi></mml:mrow> <mml:mo>∧</mml:mo></mml:msup> <mml:mi>N</mml:mi></mml:mrow> <mml:annotation>${{\rm{C}}^\wedge {\rm{N}}}$</mml:annotation> </mml:semantics> </mml:math> and <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:semantics> <mml:mrow> <mml:msup><mml:mrow><mml:mi>N</mml:mi></mml:mrow> <mml:mo>∧</mml:mo></mml:msup> <mml:mi>N</mml:mi></mml:mrow> <mml:annotation>${{\rm{N}}^\wedge {\rm{N}}}$</mml:annotation> </mml:semantics> </mml:math> 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 in silico 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 us to exploit a limited amount of experimental data to explore a wide range of chemical space, thus leveraging a modest synthetic effort. Robustness (evolution) ; Phosphorescence ; Chromophore ; Computer science ; Iridium ; Deep learning ; Artificial intelligence ; Artificial neural network ; Chemical space ; Biological system ; Materials science ; Machine learning ; Combinatorial chemistry ; Chemistry ; Drug discovery ; Photochemistry ; Physics ; Fluorescence ; Catalysis Machine Learning in Materials Science ; CO2 Reduction Techniques and Catalysts ; Radical Photochemical Reactions