Journal
JOURNAL OF THE AMERICAN CHEMICAL SOCIETY
Volume 145, Issue 44, Pages 24098-24107Publisher
AMER CHEMICAL SOC
DOI: 10.1021/jacs.3c07766
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This study presents a novel class of molecular orbital images for predicting excited-state energetics. By representing hole and particle states as channels, the model achieves excellent prediction accuracy for both singlet and triplet states.
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.
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