【特约专刊】机器学习辅助材料设计—《材料基因工程前沿（英文）》2024年第4期

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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. 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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3. 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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4. 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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5. 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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6. 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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7. 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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8. 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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9. 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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10. 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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11. 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; e75.  https://doi.org/10.1002/mgea.75

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