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Title: Data-driven design of glasses with desirable optical properties using statistical regression
Authors: Tokuda, Yomei
Fujisawa, Misa
Packwood, Daniel M.
Kambayashi, Mei
Ueda, Yoshikatsu  kyouindb  KAKEN_id  orcid (unconfirmed)
Author's alias: 徳田, 陽明
藤沢, 美沙
パックウッド, ダニエル
上林, 芽生
上田, 義勝
Issue Date: Oct-2020
Publisher: American Institute of Physics Inc.
Journal title: AIP Advances
Volume: 10
Issue: 10
Thesis number: 105110
Abstract: In this study, we used a data-driven approach to build models for assisting the design of new glasses with high refractive index and low dispersion. Our models, which are based on multiple linear regression and kernel ridge regression, achieved high accuracy in predicting optical properties of glasses based on their composition alone. Using the predictions of these models as a guide, we fabricated new glasses in our laboratory. In agreement with model predictions, these glasses had promising optical properties. This work therefore demonstrates a successful example of data-driven materials design and can be used as a template for designing glasses or other materials with other desirable properties.
Rights: © 2020 Author(s).
All article content, except where otherwise noted, is licensed under a Creative Commons Attribution (CC BY) license.
DOI(Published Version): 10.1063/5.0022451
Appears in Collections:Journal Articles

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