Tamura Ryo, Watanabe Makoto, Mamiya Hiroaki, Washio Kota, Yano Masao, Danno Katsunori, Kato Akira, Shoji Tetsuya
International Center for Materials Nanoarchitectonics, National Institute for Materials Science, Tsukuba, Japan.
Research and Services Division of Materials Data and Integrated System, National Institute for Materials Science, Tsukuba, Japan.
Sci Technol Adv Mater. 2020 Jul 29;21(1):540-551. doi: 10.1080/14686996.2020.1791676.
The relations between the mechanical properties, heat treatment, and compositions of elements in aluminum alloys are extracted by a materials informatics technique. In our strategy, a machine learning model is first trained by a prepared database to predict the properties of materials. The dependence of the predicted properties on explanatory variables, that is, the type of heat treatment and element composition, is searched using a Markov chain Monte Carlo method. From the dependencies, a factor to obtain the desired properties is investigated. Using targets of 5000, 6000, and 7000 series aluminum alloys, we extracted relations that are difficult to find via simple correlation analysis. Our method is also used to design an experimental plan to optimize the materials properties while promoting the understanding of target materials.
通过材料信息学技术提取铝合金的力学性能、热处理与元素组成之间的关系。在我们的策略中，首先利用一个准备好的数据库训练机器学习模型来预测材料性能。使用马尔可夫链蒙特卡罗方法搜索预测性能对解释变量（即热处理类型和元素组成）的依赖性。从这些依赖性中，研究获得所需性能的一个因素。以5000、6000和7000系列铝合金为目标，我们提取了通过简单相关性分析难以发现的关系。我们的方法还用于设计实验方案，以优化材料性能，同时增进对目标材料的理解。
