基于特征工程和机器学习的铝基高熵合金稳定性预测

Phase stability prediction of hign entropy alloys in aluminum matrix composites based on feature engneering and machine learning

铝基复合材料具有众多优异的性能,应用前景较好.以简单稳定相的高熵合金可以作为增强颗粒来制备铝基复合材料,其各方面力学性能都显著提升.提出了一种基于结合了特征工程和机器学习的新方法来研究高熵合金相稳定性.该方法利用特征工程筛选出影响目标属性的重要因素,然后选择相应的回归方法预测相稳定性.使用50%的数据集进行训练,并在其余数据集上进行测试验证.研究结果表明,该方法在预测高熵合金的相稳定性方面具有较高的准确性(R^(2)=0.994),且能辅助找到影响相稳定性的关键因素.

Aluminum matrix composites offer many excellent properties and wide application prospects.High entropy alloys with a simple and stable phase can be used as reinforcement to prepare aluminum matrix composites with significantly improved performance in all aspects.Herein,a new method based on feature engineering and machine learning is proposed to investigate the phase stability of high entropy alloys.This method uses feature engineering to determine the important factors affecting the target attributes,and then selects the corresponding regression method to predict the phase stability.A model on 50%of the datasets is trained and then the model is verified on other datasets.The results show that this method is highly accurate in predicting the phase stability of high entropy alloys(R^(2)=0.994).In addition,this method can be used to identify key factors affecting phase stability.