Prediction of High Entropy Alloys'Generated Phases Using Machine Learning
The preparation and test of traditional high-entropy alloys have the disadvantages of higher cost,longer cycle and some uncontrollable factors.To overcome these problems,the paper presents phase prediction of high-entropy alloys by machine learning models.Multi-classification and multi-label classification are done,and modeling is carried out by the commonly used machine learning algorithms.By analyzing the feature importance and comparing the hyper-parameter tuning,the influence of different algorithms on the prediction results are discussed.The results show that the accuracy of multi-classification support vector machine(SVM)was 0.86 and of multi-label random forest(RF)0.94.Machine learning can offer reliable synthetic property characterization,and electronegativity(△x)and valence electron concentration(VEC)can be the main factors to be considered in alloy synthesis.The combination of high-entropy alloys and machine learning is expected to provide references for improving the design of alloy systems.
万方数据
维普
