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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.

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

Keyword: Persistent homology, Ridge regression, Electron tomography, Oxygen permeability, Activation energy, Annealing temperature

Date published: 2024-05-28

Publisher: Elsevier BV

Journal:

• Micron (ISSN: 09684328) vol. 183 103664

Funding:

• JST JPMJFR213U

Manuscript type: Author's version (Accepted manuscript)

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

First published URL: https://doi.org/10.1016/j.micron.2024.103664

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Updated at: 2024-07-02 08:38:11 +0900

Published on MDR: 2026-05-28 08:37:05 +0900