| A machine-learning approach to predicting and understanding the properties of amorphous metallic alloys J Xiong, SQ Shi, TY Zhang Materials & design 187, 108378, 2020 | 295 | 2020 |
| Machine Learning of Mechanical Properties of Steels J Xiong, TY Zhang, SQ Shi SCIENCE CHINA Technological Sciences, 2020 | 168 | 2020 |
| Machine learning of phases and mechanical properties in complex concentrated alloys J Xiong, SQ Shi, TY Zhang Journal of Materials Science & Technology 87, 133-142, 2021 | 155 | 2021 |
| Machine learning prediction of elastic properties and glass-forming ability of bulk metallic glasses J Xiong, TY Zhang, SQ Shi MRS Communications, 1-10, 2019 | 99 | 2019 |
| Identifying facile material descriptors for Charpy impact toughness in low-alloy steel via machine learning Y Chen, S Wang, J Xiong, G Wu, J Gao, Y Wu, G Ma, HH Wu, X Mao Journal of Materials Science & Technology 132, 213-222, 2023 | 79 | 2023 |
| Machine learning prediction of glass-forming ability in bulk metallic glasses J Xiong, SQ Shi, TY Zhang Computational Materials Science 192, 110362, 2021 | 52 | 2021 |
| Data-driven glass-forming ability criterion for bulk amorphous metals with data augmentation J Xiong, TY Zhang Journal of Materials Science & Technology 121, 99-104, 2022 | 49 | 2022 |
| MLMD: a programming-free AI platform to predict and design materials J Ma, B Cao, S Dong, Y Tian, M Wang, J Xiong, S Sun npj Computational Materials 10 (1), 59, 2024 | 47 | 2024 |
| Gaussian process regressions on hot deformation behaviors of FGH98 nickel-based powder superalloy J Xiong, JC He, XS Leng, TY Zhang Journal of Materials Science & Technology 146, 177-185, 2023 | 38 | 2023 |
| A novel model to predict oxidation behavior of superalloys based on machine learning C Pei, Q Ma, J Zhang, L Yu, H Li, Q Gao, J Xiong Journal of Materials Science & Technology 235, 232-243, 2025 | 32 | 2025 |
| Deep learning-assisted elastic isotropy identification for architected materials A Wei, J Xiong, W Yang, F Guo Extreme Mechanics Letters 43, 101173, 2021 | 24 | 2021 |
| SISSO-assisted prediction and design of mechanical properties of porous graphene with a uniform nanopore array A Wei, H Ye, Z Guo, J Xiong Nanoscale advances 4 (5), 1455-1463, 2022 | 22 | 2022 |
| Identifying intrinsic factors for ductile-to-brittle transition temperatures in Fe–Al intermetallics via machine learning D Zhu, K Pan, HH Wu, Y Wu, J Xiong, XS Yang, Y Ren, H Yu, S Wei, ... Journal of Materials Research and Technology 26, 8836-8845, 2023 | 19 | 2023 |
| Determinants of saturation magnetic flux density in Fe-based metallic glasses: insights from machine-learning models J Xiong, BW Bai, HR Jiang, A Faus-Golfe Rare Metals 43 (10), 5256-5267, 2024 | 17 | 2024 |
| Kolmogorov–arnold network made learning physics laws simple Y Wu, T Su, B Du, S Hu, J Xiong, D Pan The Journal of Physical Chemistry Letters 15 (50), 12393-12400, 2024 | 15 | 2024 |
| Data driven discovery of an analytic formula for the life prediction of Lithium-ion batteries J Xiong, TX Lei, DM Fu, JW Wu, TY Zhang Prog. Nat. Sci.: Mater. Int. 32 (6), 793-799, 2022 | 12 | 2022 |
| Application of constitutive models and machine learning models to predict the elevated temperature flow behavior of TiAl alloy R Zhao, J He, H Tian, Y Jing, J Xiong Materials 16 (14), 4987, 2023 | 11 | 2023 |
| Pinning behavior of glycine-doped MgB2 bulks with excellent critical current density by Cu-activated low-temperature sintering Q Cai, Y Liu, Z Ma, L Yu, J Xiong, H Li Journal of alloys and compounds 585, 78-84, 2014 | 11 | 2014 |
| Tuning lattice thermal conductivity in NbMoTaW refractory high-entropy alloys: Insights from molecular dynamics using machine learning potential J Zhang, H Zhang, J Xiong, S Chen, G Zhang Journal of Applied Physics 136 (15), 2024 | 10 | 2024 |
| Design of corrosion-resistant eutectic high-entropy alloys via hybrid data-driven and expert-guided strategies S Dong, J Xiong, Y Tian, S Chen, L Wei, TY Zhang Corrosion Science, 113024, 2025 | 8 | 2025 |
Tong-yi Zhang（张统一）Shanghai UniversityVerified email at shu.edu.cn
