Prediction of electronic work function of the second phase in binary magnesium alloy based on machine learning method

• Published in: Journal of materials research Vol. 37; no. 21; pp. 3792 - 3802
• Main Authors: Wei, Xiaoxiu, Wang, Jianfeng, Wang, Chao, Zhu, Shijie, Wang, Liguo, Guan, Shaokang
• Format: Journal Article
• Language: English
• Published: Cham Springer International Publishing 14.11.2022; Springer Nature B.V
• Subjects: Applied and Technical Physics; Binary alloys; Biomaterials; Chemistry and Materials Science; Density functional theory; Inorganic Chemistry; Machine learning; Magnesium base alloys; Materials Engineering; Materials research; Materials Science; Nanotechnology; Regression analysis; Solid solutions; Support vector machines; Work functions; Second phases; Mg; Corrosion; Alloy; Machine learning

In this paper, 150 pieces of the work function of Mg-based solid solutions and Mg-containing second phases calculated by our group based on density functional theory (DFT) were collected to build a data set. According to the analysis of Pearson and Spearman correlation, 14 features were used as inpu...