Synthesis, Characterization, Supramolecular Self-Assembly, and Organic Resistive Memory Applications of BODIPY Derivatives

Li, Zuoyi; Hong, Eugene Yau‐Hin; Poon, Chun‐Ting; Cheng, Yat-Hin; Chan, Miu Shan; Leung, Ming‐Yi; Wu, Lixin; Yam, Vivian Wing‐Wah

doi:10.1021/acsmaterialslett.2c01174

Cited by 4 publications

(2 citation statements)

References 106 publications

(160 reference statements)

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“…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.…”

Section: Resultsmentioning

confidence: 99%

Encoding Hole–Particle Information in the Multi-Channel MolOrbImage for Machine-Learned Excited-State Energies of Large Photofunctional Materials

Chen,

Wing-Wah Yam

2023

J. Am. Chem. Soc.

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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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Section: Resultsmentioning

confidence: 99%

Encoding Hole–Particle Information in the Multi-Channel MolOrbImage for Machine-Learned Excited-State Energies of Large Photofunctional Materials

Chen,

Wing-Wah Yam

2023

J. Am. Chem. Soc.

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“…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].…”

Section: Introductionmentioning

confidence: 99%