Materials informatics seeks to establish microstructure–property linkage hidden in materials. A topological analysis of persistent homology and machine learning are combined to model microstructure–property linkage for dual‐phase steels, where a descriptor of persistent images is employed to characterize the microstructure and stress–strain curves are predicted using an artificial neural network. The correlations between stress and microstructure descriptor of persistent images are estimated using sensitivity analysis, Bayesian information criterion, and the least absolute shrinkage and selection operator (LASSO), respectively. The three methods identify consistent correlations, indicating that persistent images are capable of interpreting properties. Furthermore, the established artificial neural network model exhibits good accuracy and satisfactory property prediction performance. The proposed approach is expected to provide a new avenue for materials informatics and thus promote materials research.

中文翻译：

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基于持久性和机器学习的双相钢性能预测

材料信息学试图建立隐藏在材料中的微观结构与财产的联系。持久性同源性和机器学习的拓扑分析相结合，为双相钢建立了模型-属性链接的模型，其中使用了持久图像的描述符来表征组织，并使用人工神经网络预测了应力-应变曲线。分别使用敏感性分析，贝叶斯信息准则和最小绝对收缩和选择算子（LASSO）估算持续图像的应力和微观结构描述符之间的相关性。这三种方法识别一致的相关性，表明持久图像能够解释属性。此外，建立的人工神经网络模型具有良好的精度和令人满意的性能预测性能。预期所提出的方法将为材料信息学提供一条新途径，从而促进材料研究。