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

Cheng-Wei Ju; Yili Shen; Ethan J French; Jun Yi; Hongshan Bi; Aaron Tian; Zhou Lin

doi:10.1021/acs.jpca.3c07437

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

J Phys Chem A. 2024 Mar 28;128(12):2457-2471. doi: 10.1021/acs.jpca.3c07437. Epub 2024 Feb 21.

Authors

Cheng-Wei Ju^{1

2}, Yili Shen^{3

4}, Ethan J French^{1

5

6}, Jun Yi^{1

7}, Hongshan Bi¹, Aaron Tian^{3

5}, Zhou Lin¹

Affiliations

¹ Department of Chemistry, University of Massachusetts, Amherst, Massachusetts 01003, United States.
² Pritzker School of Molecular Engineering, The University of Chicago, Chicago, Illinois 60637, United States.
³ Manning College of Information and Computer Sciences, University of Massachusetts, Amherst, Massachusetts 01003, United States.
⁴ Department of Computer Science and Engineering, University of Notre Dame, Notre Dame, Indiana 46556, United States.
⁵ Department of Mathematics and Statistics, University of Massachusetts, Amherst, Massachusetts 01003, United States.
⁶ Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Charlestown, Massachusetts 02129, United States.
⁷ Department of Chemistry, Wake Forest University, Winston-Salem, North Carolina 27109, United States.

PMID: 38382058
DOI: 10.1021/acs.jpca.3c07437

Abstract

Luminescent organic semiconducting doublet-spin radicals are unique and emergent optical materials because their fluorescent quantum yields (Φ_fl) 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, 9516-9524], 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 3926 closed-shell molecules in the new training set. Interestingly, ML-ωPBE agrees with the nonempirical 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 (E_abs and E_fl) using linear-response time-dependent DFT (TDDFT), and we compared its behavior with nine popular XC functionals. For most radicals, ML-ωPBE reproduces experimental measurements of E_abs and E_fl with small MAEs of 0.299 and 0.254 eV, only marginally different from those of 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.