Density functional theory-based high-throughput materials and drug discovery has achieved tremendous success in recent decades, but its power on organic semiconducting molecules suffered catastrophically from the self-interaction error until the nonempirical but expensive optimally tuned range-separated hybrid (OT-RSH) functionals were developed. An OT-RSH transitions from a short-range (semi)­local functional to a long-range Hartree–Fock exchange at a distance characterized by a molecule-specific range-separation parameter (ω). Herein, we propose a stacked ensemble machine learning model that provides an accelerated alternative of OT-RSH based on system-dependent structural and electronic configurations. We trained ML-ωPBE, the first functional in our series, using a database of 1970 molecules with sufficient structural and functional diversity, and assessed its accuracy and efficiency using another 1956 molecules. Compared with nonempirical OT-ωPBE, ML-ωPBE reaches a mean absolute error of 0.00504a 0 –1 for optimal ω’s, reduces the computational cost by 2.66 orders of magnitude, and achieves comparable predictive power in optical properties.

High-throughput virtual materials and drug discovery based on density functional theory has achieved tremendous success in recent decades, but its power on organic semiconducting molecules suffered catastrophically from the self-interaction error until the optimally tuned range-separated hybrid (OT-RSH) exchange-correlation functionals were developed. The accurate but expensive �first-principles OT-RSH transitions from a short-range (semi-)local functional to a long-range Hartree-Fock exchange at a distance characterized by the inverse of a molecule-specific, non-empirically-determined range-separation parameter (ω). In the present study, we proposed a promising stacked ensemble machine learning (SEML) model that provides an accelerated alternative of OT-RSH based on system-dependent structural and electronic configurations. We trained ML-ωPBE, the first functional in our series, using a database of 1,970 organic semiconducting molecules with sufficient structural diversity, and assessed its accuracy and efficiency using another 1,956 molecules. Compared with the �first-principles OT-ωPBE, our ML-ωPBE reached a mean absolute error of 0:00504a_0^{-1} for the optimal value of ω, reduced the computational cost for the test set by 2.66 orders of magnitude, and achieved comparable predictive powers in various optical properties.

High-throughput virtual materials and drug discovery based on density functional theory has achieved tremendous success in recent decades, but its power on organic semiconducting molecules suffers catastrophically from self-interaction error until the optimally tuned range-separated hybrid (OT-RSH) exchange--correlation functionals were developed. The accurate but expensive fi�rst-principles OT-RSH transitions from a short-range (semi-)local functional to a long-range Hartree--Fock exchange at a distance characterized by the inverse of a molecule-specifi�c, non-empirically-determined range-separation parameter (ω). In the present study, we proposed a promising stacked ensemble machine learning model that provides an accelerated alternative of OT-RSH based on system-dependent structural and electronic con�figurations. We trained ML-ωPBE, the �first functional in our series, using a database of 1,970 organic semiconducting molecules with sufficient structural diversity, and assessed its accuracy and efficiency using another 1,956 molecules. Compared with the �first-principles OT-ωPBE, our ML-ωPBE reached a mean absolute error of 0:00504a0^{-1} for the optimal value of ω, reduced the computational cost for the test set by 2.66 orders of magnitude, and achieved a comparable predictive power in various optical properties.

Luminescent doublet-spin organic semiconducting radicals are emergent and unique candidates for organic light-emitting diodes because their internal quantum efficiency is not limited by intersystem crossing into any non-emissive high-spin state. The multi-configurational nature of their electronic structures challenges the usage of single-reference density functional theory (DFT), but the problem can be mitigated by designing more powerful exchange-correlation (XC) functionals. In an earlier study, we developed a molecule-dependent range-separated functional, referred to as ML-ωPBE, using a stacked ensemble machine learning framework. a In the present study, we assessed the performance ML-ωPBE for 64 organic semiconducting radicals from four categories, when similar radicals are absent from the training set. Compared to the firstprinciples OT-ωPBE functional, ML-ωPBE reproduced the molecule-dependent range-separation parameter, ω, with a small mean absolute error (MAE) of 0.0214 a −1 0 . Using single-reference time-dependent DFT (TDDFT), ML-ωPBE exhibited outstanding behaviors in absorption and fluorescence energies for most radicals in question, with small MAEs of 0.22 and 0.12 eV compared to experimental sources, and approached the accuracy of OT-ωPBE (0.22 and 0.11 eV). Our results demonstrated excellent generalizability and transferability of our ML-ωPBE functional from closed-shell organic semiconducting molecules to open-shell doublet-spin organic semiconducting radicals.