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Paper Abstract and Keywords
Presentation 2023-06-29 14:20
Crystal structure X-ray absorption spectrum prediction and valence ratio estimation based on Gaussian process regression
Takumi Iwashita, Haruki Hirai, Ryo Kobayashi, Tomoyuki Tamura, Masayuki Karasuyama (NIT) NC2023-3 IBISML2023-3
Abstract (in Japanese) (See Japanese page) 
(in English) X-ray absorption spectra are known as a useful experimental measurement technique for crystal structure analysis. Spectral data can provide clues to the structure of a crystal because they reflect the structural information of the arrangement of atoms in the crystal. However, since the experimentally obtained spectrum is an averaged spectrum of the crystal structure, it is not possible to separate multiple different local structures in a single crystal. Recently, simulation calculations of spectra based on theoretical calculations have become available, and structural analysis can be performed using spectra calculated from structural models, but the computational cost is very high and is not comprehensive. In this study, we propose a method based on Gaussian process regression for prediction spectrum and valence ratio estimation of crystal structure using SiO data as an example, which is a battery material. It is inferred that this ratio affects the shape of the spectrum, but this ratio cannot be directly obtained from the experimental spectrum. Therefore, we first consider the prediction spectrum for each valence ratio, using the simulation data as training data. Since each valence ratio retains structural fluctuations, we consider these fluctuations as input uncertainties and describe the construction of a prediction distribution using Gaussian process regression. Based on the estimated prediction distribution for each valence, we propose a method for inferentially inferring the valence ratio from the average spectrum of the entire crystal measured experimentally. The effectiveness of the method is verified by computer experiments.
Keyword (in Japanese) (See Japanese page) 
(in English) Gaussian Process Regression / Multitask Gaussian Process Regression / Input Uncertain Gaussian Process Regression / / / / /  
Reference Info. IEICE Tech. Rep., vol. 123, no. 91, IBISML2023-3, pp. 17-24, June 2023.
Paper # IBISML2023-3 
Date of Issue 2023-06-22 (NC, IBISML) 
ISSN Online edition: ISSN 2432-6380
Copyright
and
reproduction		 All rights are reserved and no part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopy, recording, or any information storage and retrieval system, without permission in writing from the publisher. Notwithstanding, instructors are permitted to photocopy isolated articles for noncommercial classroom use without fee. (License No.: 10GA0019/12GB0052/13GB0056/17GB0034/18GB0034)
Download PDF NC2023-3 IBISML2023-3

Conference Information
Committee NC IBISML IPSJ-BIO IPSJ-MPS  
Conference Date 2023-06-29 - 2023-07-01 
Place (in Japanese) (See Japanese page) 
Place (in English) OIST Conference Center 
Topics (in Japanese) (See Japanese page) 
Topics (in English)  
Paper Information
Registration To IBISML 
Conference Code 2023-06-NC-IBISML-BIO-MPS 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Crystal structure X-ray absorption spectrum prediction and valence ratio estimation based on Gaussian process regression 
Sub Title (in English)  
Keyword(1) Gaussian Process Regression  
Keyword(2) Multitask Gaussian Process Regression  
Keyword(3) Input Uncertain Gaussian Process Regression  
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1st Author's Name Takumi Iwashita  
1st Author's Affiliation Nagoya Institute of Technology (NIT)
2nd Author's Name Haruki Hirai  
2nd Author's Affiliation Nagoya Institute of Technology (NIT)
3rd Author's Name Ryo Kobayashi  
3rd Author's Affiliation Nagoya Institute of Technology (NIT)
4th Author's Name Tomoyuki Tamura  
4th Author's Affiliation Nagoya Institute of Technology (NIT)
5th Author's Name Masayuki Karasuyama  
5th Author's Affiliation Nagoya Institute of Technology (NIT)
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Speaker Author-1 
Date Time 2023-06-29 14:20:00 
Presentation Time 25 minutes 
Registration for IBISML 
Paper # NC2023-3, IBISML2023-3 
Volume (vol) vol.123 
Number (no) no.90(NC), no.91(IBISML) 
Page pp.17-24 
#Pages 8 
Date of Issue 2023-06-22 (NC, IBISML) 

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