{Reference Type}: Journal Article
{Title}: Prediction of nanocomposite properties and process optimization using persistent homology and machine learning.
{Author}: Uesugi F;Wen Y;Hashimoto A;Ishii M;
{Journal}: Micron
{Volume}: 183
{Issue}: 0
{Year}: 2024 Aug 28
{Factor}: 2.381
{DOI}: 10.1016/j.micron.2024.103664
{Abstract}: 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.