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2024 Journal Article Investigation of age-hardening behaviour of Al alloys via feature screening-assisted machine learningHu, 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 |
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2024 Journal Article Designing unique and high-performance Al alloys via machine learning: mitigating data bias through active learningHu, 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 |
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2023 Journal Article Recent applications of machine learning in alloy design: a reviewHu, 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 |
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2022 Journal Article Predicting the crystal structure and lattice parameters of the perovskite materials via different machine learning models based on basic atom propertiesJarin, 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 |
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2021 Journal Article Prediction of mechanical properties of wrought aluminium alloys using feature engineering assisted machine learning approachHu, 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 |
