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説明:

Physical property prediction and synthesis process optimization are key targets in material informatics. In this study, we propose a machine learning approach that utilizes ridge regression to predict the oxygen permeability at fuel cell electrode surfaces and determine the optimal process temperature. These predictions are based on a persistence diagram derived from tomographic images captured using transmission electron microscopy (TEM). Through machine learning analysis of the complex structures present in the Pt/CeO2 nanocomposites, we discovered that l2 regularization considering diverse structural elements is more appropriate than l1 regularization (sparse modeling). Notably, our model successfully captured the activation energy of oxygen permeability, a phenomenon that could not be solely explained by the geometric feature of the Betti numbers, as demonstrated in a previous study. The correspondence between the ridge regression coefficient and persistence diagram revealed the formation process of the local and three-dimensional structures of CeO2 and their contributions to pre-exponential factor and activation energies. This analysis facilitated the determination of the annealing temperature required to achieve the optimal structure and accurately predict the physical properties.

権利情報:

• Creative Commons BY-NC-ND Attribution-NonCommercial-NoDerivs 4.0 International
  

キーワード: Persistent homology, Ridge regression, Electron tomography, Oxygen permeability, Activation energy, Annealing temperature

刊行年月日: 2024-05-28

出版者: Elsevier BV

掲載誌:

• Micron (ISSN: 09684328) vol. 183 103664

研究助成金:

• JST JPMJFR213U

原稿種別: 著者最終稿 (Accepted manuscript)

MDR DOI: https://doi.org/10.48505/nims.4564

公開URL: https://doi.org/10.1016/j.micron.2024.103664

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更新時刻: 2024-07-02 08:38:11 +0900

MDRでの公開時刻: 2026-05-28 08:37:05 +0900