Prediction of the Ground-State Electronic Structure from Core-Loss Spectra of Organic Molecules by Machine Learning.

ABSTRACT: The core-loss spectrum reflects the partial density of states (PDOS) of the unoccupied states at the excited state and is a powerful analytical technique to investigate local atomic and electronic structures of materials. However, various molecular properties governed by the ground-state electronic structure of the occupied orbital cannot be directly obtained from the core-loss spectra. Here, we constructed a machine learning model to predict the ground-state carbon s- and p-orbital PDOS in both occupied and unoccupied states from the C K-edge spectra. We also attempted an extrapolation prediction of the PDOS of larger molecules using a model trained by smaller molecules and found that the extrapolation prediction performance can be improved by excluding tiny molecules. Besides, we found t

SUBMITTER: Chen PY 

PROVIDER: S-EPMC10226108 | biostudies-literature | 2023 May

REPOSITORIES: biostudies-literature