标题： 持久性是您所需的一切——微观结构表征的拓扑视角 显示英文标题

标题： Persistence is All You Need -- A Topological Lens on Microstructural Characterization

摘要： 微结构在能源和化学工程技术中使用的材料性能中起着关键作用，从催化剂和过滤器到热绝缘体和传感器。 因此，精确设计基于微观结构特征的定量描述符。 在这里，我们展示了一种单一的工作流程可以提取八个关键描述符，该流程融合了计算拓扑学与基于集成学习的回归。 首先，使用已建立的算法生成并评估了1312个合成的三维微结构，并构建了一个带有真实参数标签的数据集。 将每个结构转换为持续性图像，使我们能够训练一个深度神经网络来预测八个描述符。 在一个独立的测试集上，模型平均R^2约为0.84，皮尔逊r约为0.92，证明了其精度和泛化能力。 该方法为功能多孔材料的快速表征提供了一个统一且可扩展的工具。 显示英文摘要

摘要： The microstructure critically governs the properties of materials used in energy and chemical engineering technologies, from catalysts and filters to thermal insulators and sensors. Therefore, accurate design is based on quantitative descriptors of microstructural features. Here we show that eight key descriptors can be extracted by a single workflow that fuses computational topology with assembly-learning-based regression. First, 1312 synthetic three-dimensional microstructures were generated and evaluated using established algorithms, and a labeled data set of ground-truth parameters was built. Converting every structure into a persistence image allowed us to train a deep neural network that predicts the eight descriptors. In an independent test set, the model achieved on average R^2 ~ 0.84 and Pearson r ~ 0.92, demonstrating both precision and generality. The approach provides a unified and scalable tool for rapid characterization of functional porous materials.