Cost-Effective Multi-Channel MolOrbImage for Machine-Learned Excited-State Properties of Practical Photofunctional Materials
Ziyong Chen, Jonathan Lam, Vivian Wing-Wah Yam
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.
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…
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Taxonomy
TopicsMachine Learning in Materials Science · Advanced Chemical Physics Studies · Synthesis and Properties of Aromatic Compounds