Topological features of lithium disilicate glass-ceramics uncovered through materials informatics

Yamaguchi, Satoshi; Li, Hefei; Funayama, Naoya; Kohno, Tomoki; Imazato, Satoshi

doi:https://doi.org/10.1016/j.dental.2025.09.004

Topological features of lithium disilicate glass-ceramics uncovered through materials informatics

Yamaguchi, Satoshi; Li, Hefei; Funayama, Naoya et al.

Dental Materials, 2025

Number of Access:2（2025-11-01 15:48 Counts）

Identifier to cite or link to this item: https://hdl.handle.net/11094/102882

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Title	Topological features of lithium disilicate glass-ceramics uncovered through materials informatics
Creator	NRID 1000030397773 1000030397773 ORCID 0000-0002-4661-8821 0000-0002-4661-8821 researchmap permalink YamaguchiS_30397773 YamaguchiS_30397773 Scopus Author ID 7403873463 7403873463 CreatorName Yamaguchi, Satoshi
	CreatorName Li, Hefei
	NRID 1000001005661 1000001005661 ORCID 0009-0005-4064-0779 0009-0005-4064-0779 researchmap permalink funayama_naoya funayama_naoya Scopus Author ID 57204863265 57204863265 CreatorName Funayama, Naoya
	NRID 1000010839202 1000010839202 ORCID 0000-0002-7690-2567 0000-0002-7690-2567 researchmap permalink tomoki-kohno tomoki-kohno Scopus Author ID 57214627603 57214627603 CreatorName Kohno, Tomoki
	NRID 1000080243244 1000080243244 ORCID 0000-0003-3655-4809 0000-0003-3655-4809 researchmap permalink read0014210 read0014210 Scopus Author ID 55994423600 55994423600 CreatorName Imazato, Satoshi
Subject	Artificial Intelligence
	Ceramics
	Flexural Strength
	Machine Learning
	Materials informatics
Description	Yamaguchi S., Li H., Funayama N., et al. Topological features of lithium disilicate glass-ceramics uncovered through materials informatics. Dental Materials , (2025); https://doi.org/10.1016/j.dental.2025.09.004.
Abstract	Objective: The aim of this study was to inversely predict the topological features underlying SEM images from arbitrary biaxial flexural strengths of glass-ceramics by Materials Informatics (MI) approach. Methods: The scanning electron microscopic (SEM) image and in vitro biaxial flexural strength of 10 commercially available/experimental glass-ceramics were collected. The total of 200 SEM images were prepared as input data. Topological features underlying the SEM images were extracted using persistent homology analysis and compressed using principal component analysis. Gaussian mixture regression was employed to develop a machine learning model for predicting biaxial flexural strength based on the topological features. Arbitrary biaxial flexural strengths (390, 411, 442, 478, 515, 564, 597, 610, and 640 MPa) were defined, and an inverse analysis was conducted with the constructed machine learning model to overlay topological features onto SEM images. Results: The topological features were compressed into 18 principal components. The machine learning model was selected and optimized based on the Bayesian Information Criterion. Using the constructed machine learning model, the biaxial flexural strengths were predicted with a test score of 72 % (Root Mean Squared Error: 53.5, Mean Absolute Error: 40.3). From the arbitrary biaxial flexural strengths, topological features were inversely predicted and overlaid onto SEM images. Conclusion: The inverse analysis established in this study successfully predicted the topological features on SEM images of glass-ceramics from the biaxial flexural strengths. The MI approach with the inverse analysis promises to make the process to develop glassceramics more time-efficient than the conventional in vitro approach
Publisher	Elsevier Inc.
Source Title	Dental Materials
Date of Issued	2025-09-12
Language	English
Handle URL	https://hdl.handle.net/11094/102882
PISSN	01095641
Relation.isIdenticalTo	DOI (Publisher Version) https://doi.org/10.1016/j.dental.2025.09.004
Access Rights	open access
CopyRight	This article is licensed under a Creative Commons Attribution 4.0 International License.
oaire:version	Proof
Category	学術雑誌論文 Journal Article

Resource Type	journal article
Local Type	Journal Article
DCMI Type	Text
DCTERMS.bibliographicCitation	Dental Materials. 2025
DC.title	Topological features of lithium disilicate glass-ceramics uncovered through materials informatics
DC.creator	Yamaguchi, Satoshi
	Li, Hefei
	Funayama, Naoya
	Kohno, Tomoki
	Imazato, Satoshi
DC.publisher	Elsevier Inc.
DC.language' scheme='DCTERMS.RFC1766	English
DC.type' scheme='DCTERMS.DCMIType	Text
DCTERMS.issued' scheme='DCTERMS.W3CDTF	2025-09-12
DC.identifier	https://doi.org/10.1016/j.dental.2025.09.004
DC.subject	Artificial Intelligence
	Ceramics
	Flexural Strength
	Machine Learning
	Materials informatics
DC.description	Yamaguchi S., Li H., Funayama N., et al. Topological features of lithium disilicate glass-ceramics uncovered through materials informatics. Dental Materials , (2025); https://doi.org/10.1016/j.dental.2025.09.004.
DCTERMS.abstract	Objective: The aim of this study was to inversely predict the topological features underlying SEM images from arbitrary biaxial flexural strengths of glass-ceramics by Materials Informatics (MI) approach. Methods: The scanning electron microscopic (SEM) image and in vitro biaxial flexural strength of 10 commercially available/experimental glass-ceramics were collected. The total of 200 SEM images were prepared as input data. Topological features underlying the SEM images were extracted using persistent homology analysis and compressed using principal component analysis. Gaussian mixture regression was employed to develop a machine learning model for predicting biaxial flexural strength based on the topological features. Arbitrary biaxial flexural strengths (390, 411, 442, 478, 515, 564, 597, 610, and 640 MPa) were defined, and an inverse analysis was conducted with the constructed machine learning model to overlay topological features onto SEM images. Results: The topological features were compressed into 18 principal components. The machine learning model was selected and optimized based on the Bayesian Information Criterion. Using the constructed machine learning model, the biaxial flexural strengths were predicted with a test score of 72 % (Root Mean Squared Error: 53.5, Mean Absolute Error: 40.3). From the arbitrary biaxial flexural strengths, topological features were inversely predicted and overlaid onto SEM images. Conclusion: The inverse analysis established in this study successfully predicted the topological features on SEM images of glass-ceramics from the biaxial flexural strengths. The MI approach with the inverse analysis promises to make the process to develop glassceramics more time-efficient than the conventional in vitro approach
DC.rights	This article is licensed under a Creative Commons Attribution 4.0 International License.
DC.format	102882
citation_title	Topological features of lithium disilicate glass-ceramics uncovered through materials informatics
citation_author	Yamaguchi, Satoshi
	Li, Hefei
	Funayama, Naoya
	Kohno, Tomoki
	Imazato, Satoshi
citation_publisher	Elsevier Inc.
citation_language	English
citation_date	2025-09-12
citation_journal_title	Dental Materials
citation_doi	https://doi.org/10.1016/j.dental.2025.09.004
citation_public_url	https://hdl.handle.net/11094/102882

Topological features of lithium disilicate glass-ceramics uncovered through materials informatics

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