Bulletin of the American Physical Society
APS March Meeting 2022
Volume 67, Number 3
Monday–Friday, March 14–18, 2022; Chicago
Session S32: Deep Learning Spectroscopy
8:00 AM–10:36 AM,
Thursday, March 17, 2022
Room: McCormick Place W-192B
Sponsoring
Unit:
GDS
Chair: William Ratcliff, GDS
Abstract: S32.00001 : Prediction of materials properties from core-loss spectrum using neural network*
8:00 AM–8:36 AM
Presenter:
Teruyasu Mizoguchi
(University of Tokyo)
Author:
Teruyasu Mizoguchi
(University of Tokyo)
The group of presenter has reported applications of machine learning for XAFS/EELS spectrum. XAFS/EELS is core-loss spectroscopy observed using X-ray/electron, and the spectral feature around near edge is called XANES/ELNES and it reflects partial density of states of conduction band.
We have applied artificial neural networks for the core-loss spectrum [1-3]. For instance, the excited states that reflects the spectrum was predicted from their ground states [1], and radial distribution function was directly obtained from the XANES/ELNES [2]. In my presentation, I will mainly report on the predictions of materials properties from core-loss spectrum using the neural network. By using the C-K edge spectra as the descriptor for the neural network, totally 11 properties of organic molecules have been predicted [3].
[1] S. Kiyohara, et al., npj Comp. Mater., 6 (2020) 68-1-6.
[2] S. Kiyohara et al., J. Phys. Soc. Jpn (Letter), 89 (2020) 103001-1-4.
[3] K. Kikumasa et al., Adv. Intel. Sys., 3 (2021) 2100103-1-10.
*This research is supported by MEXT (17H06094, 19H00818, 19H05787) and JST-CREST (JPMJCR1993).
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