Accurate Electronic and Optical Properties of Organic Doublet Radicals Using Machine Learned Range-Separated Functionals

Luminescent organic semiconducting doublet-spin radicals are unique and emergent optical materials because their fluorescent quantum yields (Φfl) are not compromised by spin-flipping intersystem crossing (ISC) into any dark high-spin states. The multiconfiguration nature of these radicals challenges their electronic structure calculations in the framework of single-reference density functional theory (DFT) and introduces room for method improvement. In the present study, we extend our earlier development of ML-ωPBE, a range-separated hybrid (RSH) exchange−correlation (XC) functional constructed using the stacked ensemble machine learning (SEML) algorithm, from closed-shell organic semiconducting molecules to doublet-spin organic semiconducting radicals. We assess its performance for a new test set of 64 radicals from five categories based on the original training set of 3,926 molecules. Interestingly, ML-ωPBE agrees with the first-principles OT-ωPBE functional regarding the molecule-dependent range-separation parameter (ω), with a small mean absolute error (MAE) of 0.0197 a0−1 but saves the computational cost by 2.46 orders of magnitude. This result demonstrates outstanding domain adaptation capacity of ML-ωPBE among various organic semiconducting species. To further assess the predictive power of ML-ωPBE, we also compare its performance on absorption and fluorescence energies (Eabs and Efl) evaluated using time-dependent DFT (TDDFT) with nine conventional functionals. For most radicals, ML-ωPBE reproduces experimental measurements of Eabs and Efl with small MAEs of 0.222 and 0.121 eV, only marginally different from OT-ωPBE. Our work illustrates a successful extension of the SEML framework from closed-shell molecules to open-shell radicals and will open the venue for calculating optical properties using single-reference TDDFT.