2024第一期文章汇总

Since the proposal of the Materials Genome Initiative, the use of material repositories has gradually become a new paradigm for new material design, including thermoelectrics. In article number e21, Lu Liu, Wenqing Zhang, Lidong Chen, and Jiong Yang et al. provide a comprehensive review of the latest developments on screening of new thermoelectrics by using the MatHub-3d repository, and outline future directions for the MatHub-3d repository as well as the database-driven thermoelectrics research.

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1. 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

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2. 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

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3. 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

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4. 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

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5. 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

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6. 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

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7. 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

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8. 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

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9. 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

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