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

Po-Yen Chen; Kiyou Shibata; Katsumi Hagita; Tomohiro Miyata; Teruyasu Mizoguchi

doi:10.1021/acs.jpclett.3c00142

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

J Phys Chem Lett. 2023 May 25;14(20):4858-4865. doi: 10.1021/acs.jpclett.3c00142. Epub 2023 May 18.

Authors

Po-Yen Chen¹, Kiyou Shibata^{1

2}, Katsumi Hagita³, Tomohiro Miyata⁴, Teruyasu Mizoguchi^{1

2}

Affiliations

¹ Department of Materials Engineering, The University of Tokyo, Tokyo 113-0033, Japan.
² Institute of Industrial Science, The University of Tokyo, Tokyo 113-0033, Japan.
³ Department of Applied Physics, National Defense Academy, Yokosuka 239-0811, Japan.
⁴ Institute of Multidisciplinary Research for Advanced Materials, Tohoku University, Sendai 980-8577, Japan.

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 that using smoothing preprocess and training by specific noise data can improve the PDOS prediction for noise-contained spectra, which pave a way for the application of the prediction model to the experimental data.