论文标题
通过机器学习方法分析复杂浓缩合金中的相位形成和机械性能
Analysis of Phase Formations and Mechanical Properties in Complex Concentrated Alloys by Machine Learning Approach
论文作者
论文摘要
复合浓缩合金（CCA）的机械性能取决于它们的形成阶段和相应的结构，对给定CCA的相形成的预测对于其发现和应用至关重要。从先前的研究中收集了541个样品，其中包括61个无定形，164个单相结晶和361个多重浓度晶体CCA。我们提出了三个分类模型，以分类并了解CCA的相位选择。此外，构建了一个两位目标回归模型，以预测CCA的硬度和压缩屈服应力。所有三个分类模型的精度都高于85％，并且两个目标的随机森林回归模型的相关系数大于0.9。此外，我们通过多任务娘娘腔方法提出了四个描述符，以预测CCA的机械性能，SISSO模型的平均相关系数高于0.85。目前的工作证明了机器学习方法在预测CCA中的目标特性中的巨大潜力。
The mechanical properties of complex concentrated alloys (CCAs) depend on their forming phases and corresponding structures, the prediction of the phase formation for a given CCA is essential to its discovery and applications. 541 sample were collected from previous studies, comprising 61 amorphous, 164 single-phase crystalline, and 361 multi-phases crystalline CCAs. We proposed three classification models to category and understand the phase selection of CCAS. Also, a two-objective regression model was constructed to predict the hardness and compressive yield stress of CCAs. All three classification models have accuracies higher than 85%, and correlation coefficient of random forest regression model is greater than 0.9 for both of two objectives. In addition, we proposed four descriptors via multi-task SISSO method to predict the mechanical properties of CCAs, the average correlation coefficient of SISSO models is higher than 0.85. The present work demonstrates the great potential of machine learning approach in the prediction of target properties in CCAs.
