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.
摘要：
物理性质预测和合成过程优化是材料信息学的关键目标。在这项研究中,我们提出了一种机器学习方法，利用岭回归来预测燃料电池电极表面的氧渗透率，并确定最佳工艺温度。这些预测是基于从使用透射电子显微镜（TEM）捕获的断层摄影图像得出的持久性图。通过机器学习分析Pt/CeO2纳米复合材料中存在的复杂结构，我们发现，考虑不同结构元素的l2正则化比l1正则化（稀疏建模）更合适。值得注意的是,我们的模型成功地捕获了氧气渗透率的活化能，这种现象不能完全用贝蒂数的几何特征来解释，正如先前的研究所证明的那样。岭回归系数与持久性图之间的对应关系揭示了CeO2的局部和三维结构的形成过程及其对指数前因子和活化能的贡献。该分析有助于确定实现最佳结构和准确预测物理性质所需的退火温度。
