机器学习结合固溶强化模型预测高熵合金硬度  

Machine learning combined with solid solution strengthening model for predicting hardness of high entropy alloys

作　　者：张逸凡 任卫 王伟丽[2] 丁书剑 李楠[2] 常亮 周倩 Zhang Yi-Fan;Ren Wei;Wang Wei-Li;Ding Shu-Jian;Li Nan;Chang Liang;Zhou Qian(School of Science,Xi'an University of Posts&Telecommunications,Xi’an 710121,China;School of Physical Science and Technology,Northwestern Polytechnical University,Xi’an 710072,China)

机构地区：[1]西安邮电大学理学院,西安710121 [2]西北工业大学物理科学与技术学院,西安710072

出　　处：《物理学报》2023年第18期220-230,共11页Acta Physica Sinica

基　　金：国家自然科学基金(批准号:51931005,52171048,51571163);陕西省创新产业链项目(批准号:2020ZDLGY12-02)资助的课题。

摘　　要：第一性原理、热力学模拟等传统的材料计算方法在高熵合金的设计中多用于合金相的预测,同时会耗费巨大的计算资源.本文以性能为导向,选用机器学习的算法建立了一个高熵合金硬度预测模型,并将机器学习与固溶体强化的物理模型相结合,使用遗传算法筛选出最具有代表性的3个特征参数,利用这3个特征构建的随机森林模型,其R^(2)达到了0.9416,对高熵合金的硬度取得了较好的预测效果.本文选用的机器学习算法和3个材料特征在固溶体强化性质方面也有一定的预测效果.针对随机森林可解释性较差的问题,本文还利用SHAP可解释机器学习方法挖掘了机器学习模型的内在推理逻辑.Traditional material calculation methods,such as first principles and thermodynamic simulations,have accelerated the discovery of new materials.However,these methods are difficult to construct models flexibly according to various target properties.And they will consume many computational resources and the accuracy of their predictions is not so high.In the last decade,data-driven machine learning techniques have gradually been applied to materials science,which has accumulated a large quantity of theoretical and experimental data.Machine learning is able to dig out the hidden information from these data and help to predict the properties of materials.The data in this work are obtained from the published references.And several performance-oriented algorithms are selected to build a prediction model for the hardness of high entropy alloys.A high entropy alloy hardness dataset containing 19 candidate features is trained,tested,and evaluated by using an ensemble learning algorithm:a genetic algorithm is selected to filter the 19 candidate features to obtain an optimized feature set of 8 features;a two-stage feature selection approach is then combined with a traditional solid solution strengthening theory to optimize the features,three most representative feature parameters are chosen and then used to build a random forest model for hardness prediction.The prediction accuracy achieves an R^(2) value of 0.9416 by using the 10-fold cross-validation method.To better understand the prediction mechanism,solid solution strengthening theory of the alloy is used to explain the hardness difference.Further,the atomic size,electronegativity and modulus mismatch features are found to have very important effects on the solid solution strengthening of high entropy alloys when genetic algorithms are used for implementing the feature selection.The machine learning algorithm and features are further used for predicting solid solution strengthening properties,resulting in an R2 of 0.8811 by using the 10-fold cross-validation method.The

关 键 词：高熵合金 机器学习 遗传算法 固溶强化 

分 类 号：TG156.94[金属学及工艺—热处理] TG139[金属学及工艺—金属学] TP181[一般工业技术—材料科学与工程]