Predicting ELNES/XANES spectra by machine learning with an atomic coordinate-independent descriptor and its application to ground-state electronic structures

ELNES/XANES spectra can be observed using TEM or synchrotron radiation and can elucidate the unoccupied state electronic structures of an excited states. The computation of their features is usually demanding substantial computational resources due to the requisite structure optimization and electronic structure calculations. Herein, we leverage a machine learning technique alongside an atomic-coordinate-independent descriptor, SMILES, to yield the ELNES/XANES spectra, directly, with heightened precision. Moreover, our approach extends to obtain ground state electronic structure, namely PDOS at both occupied and unoccupied ground states, underscoring its viability for a ground-state spectroscopy. Our study revealed that incorporation of long-SMILES molecules into the training dataset enhances prediction accuracy for such molecular structures. This study's direct derivation of spectroscopy from SMILES strings holds promise for expediting spectroscopic inquiries.