2024年MGE Advances文章年度索引

引言

为方便广大读者查阅文献，Materials Genome Engineering Advances（简称：MGE Advances）期刊对2024年发表文章进行了汇总整理。欢迎各位专家学者关注、阅读和引用！

2024文章集锦

1. Editorial: Shaping the future of materials science through machine learning

Dezhen Xue,  Turab Lookman

https://onlinelibrary.wiley.com/doi/10.1002/mgea.80

引用：Xue D, Lookman T. Editorial: Shaping the future of materials science through machine learning. MGE Advances. 2024; 2(4): e80.

https://doi.org/10.1002/mgea.80

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2. The MatHub-3d first-principles repository and the applications on thermoelectrics

Lu Liu, Mingjia Yao, Yuxiang Wang, Yeqing Jin, Jialin Ji, Huifang Luo, Yan Cao, Yifei Xiong, Ye Sheng, Xin Li, Di Qiu, Lili Xi, Jinyang Xi, Wenqing Zhang*, Lidong Chen, Jiong Yang*

https://onlinelibrary.wiley.com/doi/10.1002/mgea.21

引用：Liu L, Yao M, Wang Y, et al. The MatHub-3d first-principles repository and the applications on thermoelectrics. MGE Advances. 2024; 2(1): e21.

https://doi.org/10.1002/mgea.21

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3. Navigating energy landscapes for materials discovery: Integrating modeling, simulation, and machine learning

Sajid Mannan, Vaibhav Bihani, N. M. Anoop Krishnan*, John C. Mauro*

https://onlinelibrary.wiley.com/doi/10.1002/mgea.25

引用：Mannan S, Bihani V, Krishnan NMA, Mauro JC. Navigating energy landscapes for materials discovery: integrating modeling, simulation, and machine learning. MGE Advances. 2024; 2(1): e25.

https://doi.org/10.1002/mgea.25

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4. Materials genome engineering accelerates the research and development of organic and perovskite photovoltaics

Ying Shang, Ziyu Xiong, Kang An, Jens A. Hauch, Christoph J. Brabec, Ning Li*

https://onlinelibrary.wiley.com/doi/10.1002/mgea.28

引用：Shang Y, Xiong Z, An K, Hauch JA, Brabec CJ, Li N. Materials genome engineering accelerates the research and development of organic and perovskite photovoltaics. MGE Advances. 2024; 2(1): e28.

https://doi.org/10.1002/mgea.28

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5. Applications of generative adversarial networks in materials science

Yuan Jiang, Jinshan Li*, Xiang Yang, Ruihao Yuan*

https://onlinelibrary.wiley.com/doi/10.1002/mgea.30

引用：Jiang Y, Li J, Yang X, Yuan R. Applications of generative adversarial networks in materials science. MGE Advances. 2024; 2(1): e30.

https://doi.org/10.1002/mgea.30

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6. A Review on the Applications of Graph Neural Networks in Materials Science at the Atomic-scale

Xingyue Shi, Linming Zhou, Yuhui Huang, Yongjun Wu*, Zijian Hong*

https://onlinelibrary.wiley.com/doi/10.1002/mgea.50

引用：Shi X, Zhou L, Huang Y, Wu Y, Hong Z. A review on the applications of graph neural networks in materials science at the atomic scale. MGE Advances. 2024; 2(2): e50. https://doi.org/10.1002/mgea.50

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7. Thermodynamic variational principle, its connections to the phenomenological laws and its applications to the derivation of microstructural models

Qiang Du*

https://onlinelibrary.wiley.com/doi/10.1002/mgea.51

引用：Du Q. Thermodynamicvariational principle, its connections to the phenomenological laws and its applications to the derivation of microstructural models. MGE Advances.2024; 2(2): e51. https://doi.org/10.1002/mgea.51

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8. High-throughput preparation for alloy composition design in additive manufacturing: A comprehensive review

Min Liu, Chenxu Lei, Yongxiang Wang, Baicheng Zhang*, Xuanhui Qu

https://onlinelibrary.wiley.com/doi/10.1002/mgea.55

引用：Liu M, Lei C, Wang Y, Zhang B, Qu X. High-throughput preparation for alloy composition design in additive manufacturing: a comprehensive review. MGE Advances. 2024; 2(2): e55. https://doi.org/10.1002/mgea.55

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9. Artificial intelligence enabled smart design and manufacturing of advanced materials: The endless Frontier in AI+ era

William Yi Wang, Suyang Zhang, Gaonan Li, Jiaqi Lu, Yong Ren, Xinchao Wang, Xingyu Gao, Yanjing Su, Haifeng Song*, Jinshan Li*

https://onlinelibrary.wiley.com/doi/10.1002/mgea.56

引用：Wang WY, Zhang S, Li G,et al. Artificial intelligence enabled smart design and manufacturing of advanced materials: the endless frontier in AIþ era. MGE Advances. 2024; 2(2): e56. https://doi.org/10.1002/mgea.56

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10. Harnessing quantum power: Revolutionizing materials design through advanced quantum computation

Zikang Guo, Rui Li, Xianfeng He, Jiang Guo, Shenghong Ju   

https://onlinelibrary.wiley.com/doi/10.1002/mgea.73

引用：Guo Z, Li R, He X, Guo J, Ju S. Harnessing quantum power: revolutionizing materials design through advanced quantum computation. MGE Advances. 2024; 2(4): e73. https://doi.org/10.1002/mgea.73

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11. A review on inverse analysis models in steel material design

Yoshitaka Adachi,  Ta-Te Chen,  Fei Sun,  Daichi Maruyama,  Kengo Sawai,  Yoshihito Fukatsu,  Zhi-Lei Wang

https://onlinelibrary.wiley.com/doi/10.1002/mgea.71

引用：Adachi Y, Chen T-T, Sun F, et al. A review on inverse analysis models in steel material design. MGE Advances. 2024; 2(4): e71.

https://doi.org/10.1002/mgea.71

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12. First-principle screening of corrosion resistant solutes (Al, Zn, Y, Ce, and Mn) in Mg alloys for Integrated Computational Materials Engineering guided stainless Mg design

Zhihao Yang, Junsheng Wang*, Chi Zhang, Shuo Wang, Chengpeng Xue, Guangyuan Tian, Hui Su, Chengming Yan, Zhifei Yan, Yingchun Tian

https://onlinelibrary.wiley.com/doi/10.1002/mgea.22

引用：Yang Z, Wang J, Zhang C, et al. First-principle screening of corrosion resistant solutes (Al, Zn, Y, Ce, and Mn) in Mg alloys for Integrated Computational Materials Engineering guided stainless Mg design. MGE Advances. 2024; 2(1): e22. https://doi.org/10.1002/mgea.22

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13. Effect of signal-to-noise ratio on the automatic clustering of X-ray diffraction patterns from combinatorial libraries

Yuanxun Zhou*, Biao Wu, Jianhao Wang, Hong Wang*

https://onlinelibrary.wiley.com/doi/10.1002/mgea.27

引用：Zhou Y, Wu B, Wang J, Wang H. Effect of signal-to-noise ratio on the automatic clustering of X-ray diffraction patterns from combinatorial libraries. MGE Advances. 2024; 2(1): e27. https://doi.org/10.1002/mgea.27

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14. Prediction of ultimate tensile strength of Al-Si alloys based on multimodal fusion learning

Longfei Zhu, Qun Luo, Qiaochuan Chen, Yu Zhang, Lijun Zhang, Bin Hu, Yuexing Han*, Qian Li*

https://onlinelibrary.wiley.com/doi/10.1002/mgea.26

引用：Zhu L, Luo Q, Chen Q, et al. Prediction of ultimate tensile strength of Al-Si alloys based on multimodal fusion learning. MGE Advances. 2024; 2(1): e26. https://doi.org/10.1002/mgea.26

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15. Employing deep learning in non-parametric inverse visualization of elastic–plastic mechanisms in dual-phase steels

Siyu Han, Chenchong Wang*, Yu Zhang, Wei Xu, Hongshuang Di

https://onlinelibrary.wiley.com/doi/10.1002/mgea.29

引用：Han S, Wang C, Zhang Y, Xu W, Di H. Employing deep learning in non-parametric inverse visualization of elastic–plastic mechanisms in dual-phase steels. MGE Advances. 2024; 2(1): e29. https://doi.org/10.1002/mgea.29

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16. A comparative study of machine learning in predicting the mechanical properties of the deposited AA6061 alloys via additive friction stir deposition

Qian Qiao, Quan Liu, Jiong Pu, Haixia Shi, Wenxiao Li, Zhixiong Zhu, Dawei Guo*, Hongchang Qian, Dawei Zhang, Xiaogang Li, C. T. Kwok, L. M. Tam*

https://onlinelibrary.wiley.com/doi/10.1002/mgea.31

引用：Qiao Q, Liu Q, Pu J, et al. A comparative study of machine learning in predicting the mechanical properties of the deposited AA6061 alloys via additive friction stir deposition. MGE Advances. 2024; 2(1): e31.

https://doi.org/10.1002/mgea.31

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17. Integrated Multiscale Unifying Phase-field Modellings (UPFM)

Yuhong Zhao*

https://onlinelibrary.wiley.com/doi/10.1002/mgea.44

引用：Zhao Y. Integrated unified phase-field modeling (UPFM). MGE Advances. 2024; 2(2): e44. https://doi.org/10.1002/mgea.44

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18. Design of advanced steels by integrated computational materials engineering

Xiao-Gang Lu*, Yanlin He, Weisen Zheng

https://onlinelibrary.wiley.com/doi/10.1002/mgea.36

引用：Lu X-G, He Y, Zheng W.Design of advanced steels by integrated computational materials engineering. MGE Advances. 2024; 2(2): e36.

https://doi.org/10.1002/mgea.36

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19. Development of thermodynamic database of the Mn-RE (RE=rare earth metals) binary systems

Hongjian Ye, Jiang Wang*, Qing Chen, Guanghui Rao, Huaiying Zhou

https://onlinelibrary.wiley.com/doi/10.1002/mgea.39

引用：Ye H, Wang J, Chen Q, RaoG, Zhou H. Development of thermodynamic database of the Mn-RE (RE = rare earth metals) binary systems. MGE Advances. 2024; 2(2): e39. https://doi.org/10.1002/mgea.39

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20. The anodic dissolution kinetics of Mg alloys in water based on ab initio molecular dynamics simulations

Jieqiong Yan, Xinchen Xu, Gaoning Shi, Yaowei Wang, Chaohong Guan, Yuyang Chen, Yao Yang, Tao Ying, Hong Zhu*, Qingli Tang*, Xiaoqin Zeng

https://onlinelibrary.wiley.com/doi/10.1002/mgea.47

引用：Yan J, Xu X, Shi G, et al.The anodic dissolution kinetics of Mg alloys in water based on ab initio molecular dynamics simulations. MGE Advances. 2024; 2(2): e47. https://doi.org/10.1002/mgea.47

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21. Optimal design of high-performance rare-earth-free wrought magnesium alloys by machine learning

Shaojie Li, Zaixing Dong, Jianfeng Jin*, Hucheng Pan, Zongqing Hu, Rui Hou, Gaowu Qin*

https://onlinelibrary.wiley.com/doi/10.1002/mgea.45

引用：Li S, Dong Z, Jin J, et al.Optimal design of high-performance rare-earth-free wrought magnesium alloys using machine learning. MGE Advances. 2024; 2(2): e45. https://doi.org/10.1002/mgea.45

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22. Data mining accelerated the design strategy of multi-principal element alloys with desired mechanical properties based on genetic algorithm optimization

Xianzhe Jin, Hong Luo*, Xuefei Wang, Hongxu Cheng, Chunhui Fan, Xiaogang Li

https://onlinelibrary.wiley.com/doi/10.1002/mgea.49

引用：Jin X, Luo H, Wang X, et al.Data mining accelerated the design strategy of high-entropy alloys with the largest hardness based on genetic algorithm optimization. MGE Advances. 2024; 2(2): e49.

https://doi.org/10.1002/mgea.49

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23. Bond sensitive graph neural networks for predicting high temperature superconductors 

Liang Gu, Yang Liu, Pin Chen,*, Haiyou Huang,*, Ning Chen, Yang Li, Turab Lookman, Yutong Lu, Yanjing Su*

https://onlinelibrary.wiley.com/doi/10.1002/mgea.48

引用：Gu L, Liu Y, Chen P, et al. Bond sensitive graph neural networks for predicting high temperature superconductors. MGE Advances. 2024; 2(2): e48. https://doi.org/10.1002/mgea.48

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24. Unexpected nucleation mechanism of T1 precipitates by Eshelby inclusion with unstable stacking faults

Shuo Wang, Junsheng Wang*, Chengpeng Xue, Xinghai Yang, Guangyuan Tian, Hui Su, Yisheng Miao, Quan Li, Xingxing Li

https://onlinelibrary.wiley.com/doi/10.1002/mgea.33

引用：Wang S, Wang J, Xue C,et al. Unexpected nucleation mechanism of T1precipitates by Eshelby inclusion with unstable stacking faults. MGE Advances. 2024; 2(2): e33. https://doi.org/10.1002/mgea.33

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25. An ensemble learning strategy for multi-source hydrogen embrittlement data by  introducing missing information

Xujie Gong, Ruichao Lei, Ruize Sun, Xue Jiang, Yanjing Su, Yu Yan*

https://onlinelibrary.wiley.com/doi/10.1002/mgea.35

引用：Gong X, Lei R, Sun R, Jiang X, Su Y, Yan Y. An ensemble learning strategy for multi-source hydrogen embrittlement data by introducing missing information. MGE Advances.2024; 2(2): e35.

https://doi.org/10.1002/mgea.35

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26. High-throughput study of X-ray-induced synthesis of flower-like CuxO

Qingyun Hu, Lingyue Zhu, Genmao Zhuang, Jian Hui*, Yang Ren, Hong Wang*

https://onlinelibrary.wiley.com/doi/10.1002/mgea.59

引用：Hu Q, Zhu L, Zhuang G, Wang H, Ren Y, Hui J. High-throughput study of X-ray-induced synthesis of flower-like CuxO. MGE Advances. 2024; 2(3): e59. https://doi.org/10.1002/mgea.59

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27. On the potential of using ensemble learning algorithm to approach the partitioning coefficient (k) value in Scheil-Gulliver equation

Ziyu Li*, He Tan, Anders E.W. Jarfors*, Jacob Steggo, Lucia Lattanzi, Per Jansson

https://onlinelibrary.wiley.com/doi/10.1002/mgea.46

引用：Li Z, Tan H, Jarfors AEW,Steggo J, Lattanzi L, Jansson P. On the potential of using ensemble learning algorithm to approach the partitioning coefficient (k) value in Scheil–Gulliverequation. MGE Advances. 2024; 2(3): e46. https://doi.org/10.1002/mgea.46

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28. Systematic assessment of various universal machine-learning interatomic potentials

Haochen Yu, Matteo Giantomassi, Giuliana Materzanini, Junjie Wang, Gian-Marco Rignanese*

https://onlinelibrary.wiley.com/doi/10.1002/mgea.58

引用：Yu H, Giantomassi M,Materzanini G, Wang J, Rignanese G-M. Systematic assessment of various universal machine-learning interatomic potentials. MGE Advances. 2024; 2(3): e58. https://doi.org/10.1002/mgea.58

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29. Development of a two-dimensional bipolar electrochemistry technique for high throughput corrosion screening

Yiqi Zhou*, Dirk Lars Engelberg

https://onlinelibrary.wiley.com/doi/10.1002/mgea.57

引用：Zhou Y, Engelberg DL.Development of a two-dimensional bipolar electrochemistry technique for high throughput corrosion screening. MGE Advances. 2024; 2(3): e57. https://doi.org/10.1002/mgea.57

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30. Multi-objective optimization of three mechanical properties of Mg alloys through machine learning

Wei Gou, Zhang-Zhi Shi*, Yuman Zhu, Xin-Fu Gu, Fu-Zhi Dai, Xing-Yu Gao*, Lu-Ning Wang*

https://onlinelibrary.wiley.com/doi/10.1002/mgea.54

引用：Gou W, Shi Z-Z, Zhu Y, et al. Multi-objective optimization of three mechanical properties of Mg alloys through machine learning. MGE Advances. 2024; 2(3): e54. https://doi.org/10.1002/mgea.54

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31. Machine-learning-assisted intelligent synthesis of UiO-66(Ce): Balancing the trade-off between structural defects and thermal stability for efficient hydrogenation of dicyclopentadiene

Jing Lin, Tao Ban, Tian Li, Ye Sun, Shenglan Zhou, Rushuo Li, Yanjing Su, Jitti Kasemchainan, Hongyi Gao*, Lei Shi*, Ge Wang*

https://onlinelibrary.wiley.com/doi/10.1002/mgea.61

引用：Lin J, Ban T, Li T, et al.Machine-learning-assisted intelligent synthesis of UiO-66 (Ce): balancing the trade-off between structural defects and thermal stability for efficient hydrogenation of Dicyclopentadiene. MGE Advances. 2024; 2(3): e61. https://doi.org/10.1002/mgea.61

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32. A machine learning based on crystal graph network and application in development of functional materials

Gang Xu*, You Xue, Xiaoxiao Geng, Xinmei Hou, Jinwu Xu*

https://onlinelibrary.wiley.com/doi/10.1002/mgea.38

引用：Xu G, Xue Y, Geng X, HouX, Xu J. A machine learning-based crystal graph network and its application in development of functional materials. MGE Advances. 2024; 2(3): e38. https://doi.org/10.1002/mgea.38

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33. Predicting the effect of cooling rates and initial hydrogen concentrations on porosity formation in Al-Si castings

Qinghuai Hou, Junsheng Wang*, Yisheng Miao, Xingxing Li, Xuelong Wu, Zhongyao Li, Guangyuan Tian, Decai Kong, Xiaoying Ma, Haibo Qiao, Wenbo Wang, Yuling Lang

https://onlinelibrary.wiley.com/doi/10.1002/mgea.37

引用：Hou Q, Wang J, Miao Y,et al. Predicting the effect of cooling rates and initial hydrogen concentrations on porosity formation in Al-Si castings. MGE Advances. 2024; 2(3): e37. https://doi.org/10.1002/mgea.37

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34. Accelerating spin Hall conductivity predictions via machine learning

Jinbin Zhao, Junwen Lai, Jiantao Wang, Yi-Chi Zhang, Junlin Li, Xing-Qiu Chen, Peitao Liu    

https://onlinelibrary.wiley.com/doi/10.1002/mgea.67

引用：Zhao J, Lai J, Wang J, et al. Accelerating spin Hall conductivity predictions via machine learning. MGE Advances. 2024; 2(4): e67.

https://doi.org/10.1002/mgea.67

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35. High-dimensional Bayesian optimization for metamaterial design

Zhichao Tian,  Yang Yang,  Sui Zhou,  Tian Zhou,  Ke Deng,  Chunlin Ji,  Yejun He,  Jun S. Liu   

https://onlinelibrary.wiley.com/doi/full/10.1002/mgea.79

引用：Tian Z, Yang Y, Zhou S,et al. High-dimensional Bayesian optimization for metamaterial design. MGE Advances. 2024; 2(4): e79.

https://doi.org/10.1002/mgea.79

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36. Predicting the glass transition temperature of polymer based on generative adversarial networks and automated machine learning

Zhanjie Liu,  Yixuan Huo,  Qionghai Chen,  Siqi Zhan,  Qian Li,  Qingsong Zhao,  Lihong Cui,  Jun Liu   

https://onlinelibrary.wiley.com/doi/full/10.1002/mgea.78

引用：Liu Z, Huo Y, Chen Q, et al. Predicting the glass transition temperature of polymer based on generative adversarial networks and automated machine learning. MGE Advances. 2024; 2(4): e78.

https://doi.org/10.1002/mgea.78

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37. PDGPT: A large language model for acquiring phase diagram information in magnesium alloys

Zini Yan,  Hongyu Liang,  Jingya Wang,  Hongbin Zhang,  Alisson Kwiatkowski da Silva,  Shiyu Liang,  Ziyuan Rao,  Xiaoqin Zeng

https://onlinelibrary.wiley.com/doi/full/10.1002/mgea.77

引用：Yan Z, Liang H, Wang J, et al. PDGPT: a large language model for acquiring phase diagram information in magnesium alloys. MGE Advances. 2024; 2(4): e77. https://doi.org/10.1002/mgea.77

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38. Interpretable machine learning for stability and electronic structure prediction of Janus III–VI van der Waals heterostructures

Yudong Shi, Yinggan Zhang, Jiansen Wen, Zhou Cui, Jianhui Chen, Xiaochun Huang, Cuilian Wen, Baisheng Sa, Zhimei Sun

https://onlinelibrary.wiley.com/doi/full/10.1002/mgea.76

引用：Shi Y, Zhang Y, Wen J, et al.Interpretable machine learning for stability and electronic structure prediction of Janus III–VI van der Waals heterostructures. MGE Advances. 2024; 2(4): e76.

https://doi.org/10.1002/mgea.76

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39. Machine learning-assisted performance analysis of organic photovoltaics

Sijing Zhong,  Jiayi Huang,  Hengyu Meng,  Zhuo Feng,  Qianyue Wang,  Zhenyu Huang,  Lijie Zhang,  Shiwei Li,  Weiyang Gong,  Yusen Huang,  Lei Ying,  Ning Li   

https://onlinelibrary.wiley.com/doi/full/10.1002/mgea.74

引用：Zhong S, Huang J, Meng H, et al. Machine learning-assisted performance analysis of organic photovoltaics. MGE Advances. 2024; 2(4): e74.

https://doi.org/10.1002/mgea.74

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40. Enhancing creep rupture life prediction of high-temperature titanium alloys using convolutional neural networks

Bangtan Zong, Jinshan Li, Changlu Zhou, Ping Wang, Bin Tang, Ruihao Yuan

https://doi.org/10.1002/mgea.68

引用：Zong B, Li J, Zhou C, Wang P, Tang B, Yuan R. Enhancing creep rupture life prediction of high-temperature titanium alloys using convolutional neural networks. MGE Advances. 2024; e68.

 https://doi.org/10.1002/mgea.68

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41. Prediction of dynamic recrystallization behavior of SAE52100 large section bearing steel based on machine learning

Peiheng Ding,  Changqing Shu,  Shasha Zhang,  Zhaokuan Zhang,  Xingshuai Liu,  Jicong Zhang,  Qian Chen,  Shuaipeng Yu,  Xiaolin Zhu,  Zhengjun Yao

https://onlinelibrary.wiley.com/doi/full/10.1002/mgea.75

引用：Ding P, Shu C, Zhang S, et al. Prediction of dynamic recrystallization behavior of SAE52100 large section bearing steel based on machine learning. MGE Advances. 2024; 2(4): e75. https://doi.org/10.1002/mgea.75

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【文章汇总】《材料基因工程前沿（英文）》2023年文章年度索引

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