Journal Articles
Hu, Mingwei, Tan, Qiyang, Knibbe, Ruth, Jiang, Bin, Li, Xue and Zhang, Ming-Xing (2024). Investigation of age-hardening behaviour of Al alloys via feature screening-assisted machine learning. Materials Science and Engineering: A, 916 147381, 1-12. doi: 10.1016/j.msea.2024.147381
Hu, Mingwei, Tan, Qiyang, Knibbe, Ruth, Xu, Miao, Liang, Guofang, Zhou, Jianxin, Xu, Jun, Jiang, Bin, Li, Xue, Ramajayam, Mahendra, Dorin, Thomas and Zhang, Ming-Xing (2024). Designing unique and high-performance Al alloys via machine learning: mitigating data bias through active learning. Computational Materials Science, 244 113204, 113204. doi: 10.1016/j.commatsci.2024.113204
Hu, Mingwei, Tan, Qiyang, Knibbe, Ruth, Xu, Miao, Jiang, Bin, Wang, Sen, Li, Xue and Zhang, Ming-Xing (2023). Recent applications of machine learning in alloy design: a review. Materials Science and Engineering: R: Reports, 155 100746, 100746. doi: 10.1016/j.mser.2023.100746
Jarin, Sams, Yuan, Yufan, Zhang, Mingxing, Hu, Mingwei, Rana, Masud, Wang, Sen and Knibbe, Ruth (2022). Predicting the crystal structure and lattice parameters of the perovskite materials via different machine learning models based on basic atom properties. Crystals, 12 (11) 1570, 1-21. doi: 10.3390/cryst12111570
Hu, Mingwei, Tan, Qiyang, Knibbe, Ruth, Wang, Sen, Li, Xue, Wu, Tianqi, Jarin, Sams and Zhang, Ming-Xing (2021). Prediction of mechanical properties of wrought aluminium alloys using feature engineering assisted machine learning approach. Metallurgical and Materials Transactions A: Physical Metallurgy and Materials Science, 52 (7), 2873-2884. doi: 10.1007/s11661-021-06279-5
Theses
Hu, Mingwei (2024). Design of New Wrought Aluminium Alloys with Improved Performance Assisted by Machine Learning. PhD Thesis, School of Mechanical and Mining Engineering, The University of Queensland. doi: 10.14264/500ce4a
Hu, Mingwei (2019). Development of Binder in Li-S Battery. Honours Thesis, School of Mechanical & Mining Engineering, The University of Queensland. doi: 10.14264/c6ca82a