Abstract:A new class of BODIPY conjugated molecules modified with hydrophobic alkoxy chains or hydrophilic triethylene glycol chains has been designed, synthesized, and characterized. Supramolecular self-assembly behaviors of the compounds investigated by temperature-dependent ultraviolet−visible (UV−vis) absorption studies are found to display a cooperative growth mechanism with the formation of uniform and well-defined morphology of the aggregates in THF and water mixtures, which have been studied by transmission ele… Show more
“…As a final test, the optimal ϵV H V P +rVGG model has been applied (Figure and Tables S6 and S7) to a series of 30 organic photofunctional materials reported by Yam and co-workers. − These test molecules are not included in the CSD data set. In addition to the high correlation ( r > 0.95, Figure b and c), the ML-predicted excited-state energies for both low-lying singlet and triplet states exhibit notably low MAEs (<0.14 eV, Figure d) in comparison to the PBE0-computed references.…”
We present a novel class of one-electron multi-channel
molecular
orbital images (MolOrbImages) designed for the prediction of excited-state
energetics in conjunction with the state-of-the-art VGG-type machine-learning
architecture. By representing hole and particle states in the excitation
process as channels of MolOrbImages, the revised VGG model achieves
excellent prediction accuracy for both low-lying singlet and triplet
states, with mean absolute errors (MAEs) of <0.08 and <0.1 eV
for QM9 molecules and large photofunctional materials with up to 560
atoms, respectively. Remarkably, the model demonstrates exceptional
performance (MAE < 1 kcal/mol) for the T1 state of QM9
molecules, making it a non-system-specific model that approaches chemical
accuracy. The general rules attained, for instance, the improved performance
with well-defined MO energies and the reduced overfitting concern
via the inclusion of physically insightful hole–particle information,
provide invaluable guidelines for the further design of orbital-based
descriptors targeting molecular excited states.
“…As a final test, the optimal ϵV H V P +rVGG model has been applied (Figure and Tables S6 and S7) to a series of 30 organic photofunctional materials reported by Yam and co-workers. − These test molecules are not included in the CSD data set. In addition to the high correlation ( r > 0.95, Figure b and c), the ML-predicted excited-state energies for both low-lying singlet and triplet states exhibit notably low MAEs (<0.14 eV, Figure d) in comparison to the PBE0-computed references.…”
We present a novel class of one-electron multi-channel
molecular
orbital images (MolOrbImages) designed for the prediction of excited-state
energetics in conjunction with the state-of-the-art VGG-type machine-learning
architecture. By representing hole and particle states in the excitation
process as channels of MolOrbImages, the revised VGG model achieves
excellent prediction accuracy for both low-lying singlet and triplet
states, with mean absolute errors (MAEs) of <0.08 and <0.1 eV
for QM9 molecules and large photofunctional materials with up to 560
atoms, respectively. Remarkably, the model demonstrates exceptional
performance (MAE < 1 kcal/mol) for the T1 state of QM9
molecules, making it a non-system-specific model that approaches chemical
accuracy. The general rules attained, for instance, the improved performance
with well-defined MO energies and the reduced overfitting concern
via the inclusion of physically insightful hole–particle information,
provide invaluable guidelines for the further design of orbital-based
descriptors targeting molecular excited states.
“…The RS effect is mostly observed in inorganic materials [7]- [10]. However, RRAMs based on organic semiconductors were recently shown to be promising [11]- [15].…”
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