| Recent applications of machine learning in alloy design: A review M Hu, Q Tan, R Knibbe, M Xu, B Jiang, S Wang, X Li, MX Zhang Materials Science and Engineering: R: Reports 155, 100746, 2023 | 49 | 2023 |
| Prediction of mechanical properties of wrought aluminium alloys using feature engineering assisted machine learning approach M Hu, Q Tan, R Knibbe, S Wang, X Li, T Wu, S Jarin, MX Zhang Metallurgical and Materials Transactions A 52 (7), 2873-2884, 2021 | 40 | 2021 |
| Predicting the crystal structure and lattice parameters of the perovskite materials via different machine learning models based on basic atom properties S Jarin, Y Yuan, M Zhang, M Hu, M Rana, S Wang, R Knibbe Crystals 12 (11), 1570, 2022 | 19 | 2022 |
| Designing unique and high-performance Al alloys via machine learning: Mitigating data bias through active learning M Hu, Q Tan, R Knibbe, M Xu, G Liang, J Zhou, J Xu, B Jiang, X Li, ... Computational Materials Science 244, 113204, 2024 | 2 | 2024 |
| Investigation of age-hardening behaviour of Al alloys via feature screening-assisted machine learning M Hu, Q Tan, R Knibbe, B Jiang, X Li, MX Zhang Available at SSRN 4917474, 2024 | | 2024 |
