摘要
提出预测面心立方高熵合金弹性性能的机器学习模型。数据集来源于第一性原理计算得到的186组样本。测试集体积模量(B)和剪切模量(G)预测值的拟合优度(R2)分别达到0.81和0.84。根据机器学习结果,Co Ni CuMoW在等主元高熵合金(G/B≤0.57)中具有最高的B、G和弹性模量(Y)和良好的塑性。第一性原理计算结果表明,当W含量增加时,(CoNiCuMo)1-xWx合金的弹性各向异性提高,且塑性下降。差分电荷密度分析结果表明,W—W和W—Mo键存在明显的电荷聚集,表明W原子与邻近原子间存在共价键作用。
The machine learning(ML)models were proposed for predicting elastic properties of face-centered-cubic(FCC)high-entropy alloys(HEAs).The data set was from the first-principles calculation,which contained 186 samples.The goodness-of-fit(R~2)values of predicted bulk modulus(B)and shear modulus(G)in the test set were 0.81 and 0.84,respectively.According to the results of ML,Co Ni Cu Mo W HEAs have the largest B,G,elastic modulus(Y)and good ductility(G/B≤0.57)among the FCC HEAs with equal components.The first-principles calculation results show that the elastic anisotropy of(Co Ni Cu Mo)1-xWx HEAs increases and ductility decreases with increasing W content.According to the analysis of charge density difference,there is obvious charge accumulation at W—W and W—Mo bonds,indicating the directional covalent bonds formed between W atoms and their neighboring atoms.
作者
王申
李达
熊俊
Shen WANG;Da LI;Jun XIONG(Key Laboratory of Advanced Technologies of Materials,Ministry of Education,College of Materials Science and Engineering,Southwest Jiaotong University,Chengdu 610031,China)
基金
the financial support from the National Natural Science Foundation of China(Nos.62173280,51975491)。
关键词
弹性模量
面心立方高熵合金
第一性原理计算
机器学习
elastic modulus
face-centered cubic high-entropy alloys
first-principles calculations
machine learning