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Cost-Effective Multi-Channel MolOrbImage for Machine-Learned Excited-State Properties of Practical Photofunctional

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We developed a faster method to predict excited-state energies of photofunctional materials using multi-channel molecular orbital images (MolOrbImage). This approach significantly reduces computational cost while maintaining high accuracy for materials discovery.

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Area of Science:

  • Computational chemistry
  • Materials science
  • Quantum chemistry

Background:

  • Predicting excited-state energies is crucial for photofunctional materials.
  • High computational cost of traditional methods limits high-throughput discovery.
  • Multi-channel molecular orbital images (MolOrbImage) offer a promising approach.

Purpose of the Study:

  • To develop a cost-effective method for predicting excited-state energies.
  • To overcome the computational limitations of mean-field ground-state calculations.
  • To enable high-throughput discovery of novel photofunctional materials.

Main Methods:

  • Incorporated hole and particle information into MolOrbImage.
  • Employed low-cost orbital generation techniques (superposition of atomic densities, semiempirical tight-binding).
  • Utilized convolutional neural networks for prediction and perturbation analysis.

Main Results:

  • Achieved high accuracy for small organic molecules (MAE < 0.1 eV).
  • Demonstrated accuracy for practical photofunctional materials (MAE < 0.14 eV).
  • Identified frontier orbital energies as key predictors.

Conclusions:

  • The developed method significantly reduces computational cost for excited-state energy prediction.
  • MolOrbImage combined with CNNs is effective for both small molecules and complex materials.
  • Transfer learning can further enhance prediction accuracy.