ABSTRACT 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.

中文翻译：

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了解铝合金的材料信息学方法

摘要 通过材料信息学技术提取了铝合金中力学性能、热处理和元素组成之间的关系。在我们的策略中，机器学习模型首先由准备好的数据库训练以预测材料的特性。使用马尔可夫链蒙特卡罗方法搜索预测属性对解释变量的依赖性，即热处理类型和元素组成。根据相关性，研究了获得所需特性的因素。使用 5000、6000 和 7000 系列铝合金的目标，我们通过简单的相关性分析提取了难以找到的关系。我们的方法还用于设计实验计划以优化材料特性，同时促进对目标材料的理解。