基于机器学习方法的复杂浓固溶体合金相形成与力学性能分析
Analysis of Phase Formations and Mechanical Properties in Complex Concentrated Alloys by Machine Learning Approach
AI总结：
本文利用机器学习方法，通过三种分类模型和双目标回归模型及多任务SISSO描述符，对复杂浓固溶体合金的相形成、硬度和压缩屈服应力进行预测，分类准确率超85%，回归相关系数超0.85，验证了机器学习在该领域的应用潜力。
摘要：
复杂浓固溶体合金（CCAs）的力学性能取决于其形成的相及相应结构，因此对给定CCA的相形成进行预测对其发现和应用至关重要。本研究从以往研究中收集了541个样本，包括61个非晶态、164个单相晶体和361个多相晶体CCAs。我们提出了三种分类模型来对CCAs的相选择进行分类和理解。同时，构建了一个双目标回归模型来预测CCAs的硬度和压缩屈服应力。三种分类模型的准确率均高于85%，随机森林回归模型对两个目标的相关系数均大于0.9。此外，我们通过多任务SISSO方法提出了四个描述符来预测CCAs的力学性能，SISSO模型的平均相关系数高于0.85。本工作证明了机器学习方法在预测CCAs目标性能方面的巨大潜力。
英文摘要：
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.
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