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

## TL;DR

This paper introduces a cost-effective method to predict excited-state energies of photofunctional materials using machine learning and multi-channel molecular orbital images.

## Contribution

The novel approach uses low-cost orbital approximations to enable efficient and accurate predictions of excited-state properties.

## Key findings

- The model achieves MAE < 0.1 eV for small organic molecules and MAE < 0.14 eV for practical photofunctional materials.
- Frontier orbital energies are identified as crucial for accurate predictions through perturbation analysis.
- Transfer learning is proposed to further reduce prediction errors in excited-state energy calculations.

## Abstract

Leveraging our recent
development, which incorporates
hole and
particle information into the multi-channel molecular orbital image
(MolOrbImage), to generate exceptional accuracy (mean absolute error,
MAE < 0.1 eV) in predicting excited-state energies of practical
photofunctional materials containing several hundred atoms, we have
advanced the implementation of a new approach to overcome the high
computational cost of mean-field ground-state calculations that limits
its application in high-throughput materials discovery. In this work,
low-cost approaches for generating approximate orbitals, including
the superposition of atomic densities technique and the semiempirical
tight-binding method, have been employed to construct cost-effective
multi-channel MolOrbImages. By connecting with a convolutional neural
network, the performance of our model is evaluated for both small
organic molecules (MAE < 0.1 eV) and practical photofunctional
materials (MAE < 0.14 eV). Perturbation analysis of MolOrbImages
highlights the importance of frontier orbital energies, which further
motivates the adoption of transfer learning techniques to reduce prediction
errors in excited-state energies.