Dự đoán chức năng điện tử của pha thứ hai trong hợp kim magie nhị nguyên dựa trên phương pháp học máy

Journal of Materials Research - Tập 37 - Trang 3792-3802 - 2022
Xiaoxiu Wei1, Jianfeng Wang1, Chao Wang1, Shijie Zhu1, Liguo Wang1, Shaokang Guan1
1School of Materials Science and Engineering, Zhengzhou University, Zhengzhou, China

Tóm tắt

Trong bài báo này, 150 giá trị của chức năng công việc của các dung dịch rắn chứa Mg và các pha thứ hai chứa Mg đã được nhóm chúng tôi tính toán dựa trên lý thuyết chức năng mật độ (DFT) được thu thập để xây dựng một bộ dữ liệu. Theo phân tích tương quan Pearson và Spearman, 14 đặc trưng đã được sử dụng làm biến đầu vào. Bốn mô hình học máy (ML) bao gồm hồi quy tuyến tính đa biến, hồi quy vector hỗ trợ (SVR), cây hồi quy tăng cường độ dốc và cây hồi quy tăng cường cực đoan đã được thiết lập để dự đoán chức năng công việc của các pha thứ hai. Kết quả cho thấy mô hình SVR có độ chính xác cao nhất và khả năng tổng quát tốt nhất trong việc dự đoán chức năng công việc. Chức năng công việc của pha Mg7Zn3 dự đoán từ mô hình SVR rất gần với giá trị tính toán từ DFT, điều này cho thấy ML là một phương pháp hiệu quả để dự đoán chức năng công việc trong hợp kim Mg.

Từ khóa

#hợp kim magie #chức năng công việc #học máy #hồi quy #lý thuyết chức năng mật độ

Tài liệu tham khảo

F. Witte, The history of biodegradable magnesium implants: a review. Acta Biomater. 6, 1680–1692 (2010) Y.F. Zheng, X.N. Gu, F. Witte, Biodegradable metals. Mater. Sci. Eng. R 77, 1–34 (2014) X.N. Gu, S.S. Li, X.M. Li, Y.B. Fan, Magnesium based degradable biomaterials: a review. Front. Mater. Sci. 8, 200–218 (2014) G.L. Song, A. Atrens, Corrosion mechanisms of magnesium alloys. Adv. Eng. Mater. 1, 11–33 (1999) M. Ascencio, M. Pekguleryuz, S. Omanovic, An investigation of the corrosion mechanisms of WE43 Mg alloy in a modified simulated body fluid solution: the influence of immersion time. Corros. Sci. 87, 489–503 (2014) M.P. Staiger, A.M. Pietak, J. Huadmai, G. Dias, Magnesium and its alloys as orthopedic biomaterials: a review. Biomaterials 27, 1728–1734 (2006) Y.C. Xin, K.F. Huo, H. Tao, G.Y. Tang, P.K. Chu, Influence of aggressive ions on the degradation behavior of biomedical magnesium alloy in physiological environment. Acta Biomater. 4, 2008–2015 (2008) D. Lee, B. Kim, S. Lee, S.M. Baek, J.C. Kim, H.T. Son, J.G. Lee, K.S. Lee, S.S. Park, Enhanced corrosion resistance of Mg–Sn–Zn–Al alloy by Y microalloying. Scr. Mater. 163, 125–129 (2019) B. Wang, S.K. Guan, J. Wang, L.G. Wang, S.J. Zhu, Effects of Nd on microstructures and properties of extruded Mg–2Zn–0.46Y–xNd alloys for stent application. Mater. Sci. Eng. B 176, 1673–1678 (2011) W.C. Kim, J.G. Kim, J.Y. Lee, H.K. Seok, Influence of Ca on the corrosion properties of magnesium for biomaterials. Mater. Lett. 62, 4146–4148 (2008) L. Fu, Q.C. Le, Y. Tang, J.Y. Sun, Y.H. Jia, Z.T. Song, Effect of Ca and RE additions on microstructures and tensile properties of AZ31 alloys. Mater. Res. Express. 5, 056521 (2018) S. Agarwal, J. Curtin, B. Duffy, S. Jaiswal, Biodegradable magnesium alloys for orthopaedic applications: a review on corrosion, biocompatibility and surface modifications. Mater. Sci. Eng. C 68, 948–963 (2016) S.Q. Yin, W.C. Duan, W.H. Liu, L. Wu, J.M. Yu, Z.L. Zhao, M. Liu, P. Wang, J.Z. Cui, Z.Q. Zhang, Influence of specific second phases on corrosion behaviors of Mg-Zn-Gd-Zr alloys. Corros. Sci. 166, 108419 (2020) N.D. Lang, W. Kohn, Theory of metal surfaces: work function. Phys. Rev. B 3, 1215–1223 (1971) J. Wang, S.Q. Wang, Surface energy and work function of fcc and bcc crystals: density functional study. Surf. Sci. 630, 216–224 (2014) U. Konig, B. Davepon, Microstructure of polycrystalline Ti and its microelectrochemical properties by means of electron-backscattering diffraction (EBSD). Electrochim. Acta 47, 149–160 (2001) K.S. Shin, M.Z. Bian, N.D. Nam, Effects of crystallographic orientation on corrosion behavior of magnesium single crystals. JOM 64, 664–670 (2012) H. Ma, X.Q. Chen, R.H. Li, S.L. Wang, J.H. Dong, W. Ke, First-principles modeling of anisotropic anodic dissolution of metals and alloys in corrosive environments. Acta Mater. 130, 137–146 (2017) Y.H. Hou, G. Xiong, L.L. Liu, G.Q. Li, N. Moelans, M.X. Guo, Effects of LaAlO3 and La2O2S inclusions on the initialization of localized corrosion of pipeline steels in NaCl solution. Scr. Mater. 177, 151–156 (2020) C. Xu, J.F. Wang, C. Chen, C. Wang, Y.F. Sun, S.J. Zhu, S.K. Guan, Initial micro-galvanic corrosion behavior between Mg2Ca and α-Mg via quasi-in situ SEM approach and first-principles calculation. J. Magnes. Alloy. (2021). https://doi.org/10.1016/j.jma.2021.06.017 K. Kokko, P.T. Salo, R. Laihia, K. Mansikka, First-principles calculations for work function and surface energy of thin lithium films. Surf. Sci. 348, 168–174 (1996) N.E. Singh-Miller, N. Marzari, Surface energies, work functions, and surface relaxations of low-index metallic surfaces from first principles. Phys. Rev. B 80, 235407 (2009) E. Lin, H.Y. Lane, Machine learning and systems genomics approaches for multi-omics data. Biomark. Res. 5, 2 (2017) P. Mamoshina, A. Vieira, E. Putin, A. Zhavoronkov, Applications of deep learning in biomedicine. Mol. Pharm. 13, 1445–1454 (2016) T. Chen, Q. Gao, Y. Yuan, T. Li, Q. Xi, T. Liu, A. Tang, A. Watson, F. Pan, Coupling physics in machine learning to investigate the solution behavior of binary Mg alloys. J. Magnes. Alloys (2021). https://doi.org/10.1016/j.jma.2021.06.014 Z.N. Tong, L.Y. Wang, G.M. Zhu, X.Q. Zeng, Predicting twin nucleation in a polycrystalline Mg alloy using machine learning methods. Metall Mater Trans A 50, 5543–5560 (2019) Z.R. Pei, J.Q. Yin, Machine learning as a contributor to physics: understanding Mg alloys. Mater. Des. 172, 107759 (2019) B. Meredig, A. Agrawal, S. Kirklin, J.E. Saal, J.W. Doak, A. Thompson, K. Zhang, A. Choudhary, C. Wolverton, Combinatorial screening for new materials in unconstrained composition space with machine learning. Phys. Rev. B 89, 094104 (2014) C. Wang, J.F. Wang, D. Ma, S.J. Zhu, L.G. Wang, S.K. Guan, First-principles studies on structure stability, segregation, and work function of Mg doped with metal elements. Int. J. Quantum Chem. 121, e26626 (2021) M.A. Lahmer, First-principles study of the structural and electronic properties of the clean and O-deficient ZnAl2O4(111) surfaces. Surf. Sci. 682, 75–83 (2019) D.P. Ji, Q.X. Zhu, S.Q. Wang, First-principles study of the structural and electronic properties of the clean and O-deficient ZnAl2O4(111) surfaces. Surf. Sci. 651, 137–146 (2016) G. Kresse, J. Furthmuller, Efficient iterative schemes for ab initio total-energy calculations using a plane-wave basis set. Phys. Rev. B 54, 11169–11186 (1996) P.E. Blochl, Projector augmented-wave method. Phys. Rev. B 50, 17953–17979 (1994) J.P. Perdew, K. Burke, M. Ernzerhof, Generalized gradient approximation made simple. Phys. Rev. Lett. 77, 3865–3868 (1996) L.C. Yan, Y.P. Diao, Z.Y. Lang, K.W. Gao, Corrosion rate prediction and influencing factors evaluation of low-alloy steels in marine atmosphere using machine learning approach. Sci. Technol. Adv. Mater. 21, 359–370 (2020) D. Shin, Y. Yamamoto, M.P. Brady, S. Lee, J.A. Haynes, Modern data analytics approach to predict creep of high-temperature alloys. Acta Mater. 168, 321–330 (2019) D.C. Luor, A comparative assessment of data standardization on support vector machine for classification problems. Intell. Data Anal. 19, 529–546 (2015) H.H. Xu, Y. Deng, Dependent evidence combination based on Shearman coefficient and Pearson coefficient. IEEE Access 6, 11634–11640 (2018) P. Sedgwick, Pearson’s correlation coefficient. BMJ 344, e4483 (2012) J. Hauke, T. Kossowski, Comparison of values of Pearson’s and Spearman’s correlation coefficients on the same sets of data. Quaest. Geogr. 30, 87–93 (2011) K.J. Preacher, P.J. Curran, D.J. Bauer, Computational tools for probing interactions in multiple linear regression, multilevel modeling, and latent curve analysis. J. Educ. Behav. Stat. 31, 437–448 (2006) A.J. Smola, B. Scholkopf, A tutorial on support vector regression. Stat. Comput. 14, 199–222 (2004) A. Natekin, A. Knoll, Gradient boosting machines, a tutorial. Front. Neurorobotics 7, 21 (2013) X. Chen, L. Huang, D. Xie, Q. Zhao, Egbmmda: extreme gradient boosting machine for mirna-disease association prediction. Cell Death Dis. 9, 3 (2018) X.N. Xu, L.Y. Wang, G.M. Zhu, X.Q. Zeng, Predicting tensile properties of AZ31 magnesium alloys by machine learning. JOM 72, 3935–3942 (2020) J.C. Ren, ANN vs. SVM: which one performs better in classification of MCCs in mammogram imaging. Knowl. Based Syst. 26, 144–153 (2012) G.B. Huang, Q.Y. Zhu, C.K. Siew, Extreme learning machine: theory and applications. Neurocomputing 70, 489–501 (2006) T. Fushiki, Estimation of prediction error by using K-fold cross-validation. Stat. Comput. 21, 137–146 (2011) F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg, J. Vanderplas, A. Passos, D. Cournapeau, M. Brucher, M. Perrot, E. Duchesnay, Scikit-learn: machine learning in Python. J. Mach. Learn. Res. 12, 2825–2830 (2011) Z.B. Pei, D.W. Zhang, Y.J. Zhi, T. Yang, L.L. Jin, D.M. Fu, X.Q. Cheng, H.A. Terryn, J.M.C. Mol, X.G. Li, Towards understanding and prediction of atmospheric corrosion of an Fe/Cu corrosion sensor via machine learning. Corros. Sci. 170, 108697 (2020) Y. Liu, T.L. Zhao, W.W. Ju, S.Q. Shi, Materials discovery and design using machine learning. J. Materiomics 3, 159–177 (2017)