Identification of compositions that form solid solution phases from a large compositional space is challenging. The current study focuses on applying multiple machine learning classification algorithms for suitably predicting new medium to high entropy alloys with solid solution phases. The current data set used for training and testing consisting of 664 labeled data with 267 BCC alloys, 199 FCC alloys, and 198 (FCC + BCC) alloys to avoid biased predicting. The analyzed data shows a strong correlation between the empirical design parameters. The correlation coefficient values changed while moving the alloy system from medium to high entropy domain. The parameters VEC and T
m
show high importance in prediction compared to other parameters. The importance of design parameters is analyzed, and the accuracy is quantified. The experimental results validate the prediction of a shift from BCC + FCC to FCC phases while increasing Ni content in the CoCuFeNi
x
system.
中文翻译：
从大的组成空间中鉴定形成固溶体相的组成是具有挑战性的。目前的研究侧重于应用多种机器学习分类算法来适当地预测具有固溶体相的新中高熵合金。当前用于训练和测试的数据集由 664 个标记数据组成，其中包含 267 种 BCC 合金、199 种 FCC 合金和 198 种 (FCC + BCC) 合金，以避免出现偏差预测。分析的数据显示了经验设计参数之间的强相关性。相关系数值随着合金系统从中熵域向高熵域移动而发生变化。参数VEC和T
米
与其他参数相比，在预测中显示出高度的重要性。分析了设计参数的重要性，并对精度进行了量化。实验结果验证了从 BCC + FCC 相转变为 FCC 相的预测，同时增加了 CoCuFeNi
x
系统中的 Ni 含量。
