Abstract
Luminescent organic semiconducting doublet-spin radicals are unique and emergent optical materials because their fluorescent quantum yields (Φfl) are not compromised by the spin-flipping intersystem crossing (ISC) into a dark high-spin state. The multiconfigurational 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 extended our earlier development of ML-ωPBE [J. Phys. Chem. Lett., 2021, 12, 9516], 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 assessed its performance for a new test set of 64 doublet-spin radicals from five categories while placing all previously compiled 3,926 closed-shell molecules in the new training set. Interestingly,
ML-ωPBE agrees with the first-principles OT-ωPBE functional regarding the prediction of 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 an outstanding domain adaptation capacity of ML-ωPBE for diverse organic semiconducting species. To further assess the predictive power of ML-ωPBE in experimental observables, we also applied it to evaluate absorption and fluorescence energies (Eabs and Efl), using linear-response time-dependent DFT (TDDFT) and compared its behavior with nine popular XC functionals. For most radicals, ML-ωPBE reproduces experimental measurements of Eabs and Efl with small MAEs of 0.299 and 0.254 eV, only marginally different from OT-ωPBE. Our work illustrates a successful extension of the SEML framework from closed-shell molecules to doublet-spin radicals and will open the venue for calculating optical properties for organic semiconductors using single-reference TDDFT.
Supplementary materials
Title
Supporting Information: Accurate Electronic and Optical Properties of Organic Doublet Radicals Using Machine Learned Range-Separated Functionals
Description
Brief revisit of the SEML model; error statistics of ML-ωPBE and other XC functionals in optical properties; and configurations of frontier MOs and NTOs (PDF).
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Title
Molecular Geometries in XYZ Coordinates
Description
Optimized D0 geometries for 48 radicals in the test subset of Eabs; and optimized D1 geometries for 16 radicals in the test subset of Efl (ZIP).
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Title
SMILES Strings, Range Separation Parameters, and Optical Band Gaps
Description
SMILES strings, experimental measurements and TDDFT calculations for Eabs and Efl, values of ωOT and ωML, and static and optical dielectric constants for all 64 radicals in the external test set (XLSX).
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Supplementary weblinks
Title
Stacked Ensemble Machine Learning for Range-Separation Parameters
Description
Source codes and original dataset for the stacked ensemble machine learning (SEML) model in the construction of ML-wPBE functional developed in the Lin group.
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