A message passing neural network for predicting dipole moment dependent core electron excitation spectra

Abstract

Absorption near-edge structures in core electron excitation spectra reflect the anisotropy of orbitals in the final transition state and can be utilized for analyzing the local atomic environment, including its orientation. So far, the analysis of fine structures has primarily relied on fingerprint-matching with high-cost experimental or simulated spectra. If core electron excitation spectra, including their anisotropy, can be predicted at a low cost using machine learning, the application range of these spectra will be accelerated and extended to areas such as the orientation and electronic structure analysis of liquid crystals and organic solar cells at high spatial resolution. In this study, we introduce a message-passing neural network, named inversion symmetry-aware directional PaiNN (ISD-PaiNN) for predicting core electron excitation spectra using a unit direction vector in addition to molecular graphs as the input. Utilizing a database of calculated C K-edge spectra, we have confirmed that the network can predict core electron excitation spectra reflecting the anisotropy of molecules. Our model is expected to be expanded to other physical quantities in general that depend not only on molecular graphs but also on anisotropic vectors.