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 multi-configurational nature of radical electronic structures challenges computational studies in the framework of single-reference density functional theory (DFT) and introduces room for method improvement. In the present study, we extended our earlier development of a machine-learned range-separated hybrid functional, referred to as ML-ωPBE, from closed-shell molecules to doublet-spin radicals, and assessed its performance for the original training set of 3,926 organic semiconducting molecules and an external test set of 64 organic semiconducting radicals from five categories. Interestingly, for this external test set, ML-ωPBE reproduced the optimal value of ω, the molecule-dependent range-separation parameter, from the first-principles OT-ωPBE functional with a small mean absolute error (MAE) of 0.0197 a0−1 and with a significant save of computational cost by 2.46 orders of magnitude. This result demonstrated excellent generalizability and transferability of ML-ωPBE among a variety of organic semiconducting species. To further assess the predictive power of ML-ωPBE on organic semiconducting radicals, we also compared its performance on experimentally measurable absorption and fluorescence energies (Eabs’s and Efl’s), evaluated using time-dependent DFT (TDDFT), with nine conventional functionals. ML-ωPBE reproduced experimental Eabs’s and Efl’s for most radicals in questions, with small MAEs of 0.222 and 0.121 eV, marginally worse from OT-ωPBE. Our work not only illustrated a successful extension of stacked ensemble machine learning (SEML) framework from closed-shell molecules to open-shell doublet-spin radicals, but also opened the venue for the calculations of optical properties these using single-reference TDDFT.