Dramatically enhanced combination of ultimate tensile strength and electric conductivity of alloys via machine learning screening H Zhang, H Fu, X He, C Wang, L Jiang, LQ Chen, J Xie Acta Materialia 200, 803-810, 2020 | 197 | 2020 |
A property-oriented design strategy for high performance copper alloys via machine learning C Wang, H Fu, L Jiang, D Xue, J Xie npj Computational Materials 5 (1), 87, 2019 | 177 | 2019 |
Effect of rolling and aging processes on microstructure and properties of Cu-Cr-Zr alloy H Fu, S Xu, W Li, J Xie, H Zhao, Z Pan Materials Science and Engineering: A 700, 107-115, 2017 | 177 | 2017 |
Machine learning assisted composition effective design for precipitation strengthened copper alloys H Zhang, H Fu, S Zhu, W Yong, J Xie Acta Materialia 215, 117118, 2021 | 155 | 2021 |
Effect of Ag addition on the microstructure and mechanical properties of Cu-Cr alloy S Xu, H Fu, Y Wang, J Xie Materials Science and Engineering: A 726, 208-214, 2018 | 115 | 2018 |
Discovery of aluminum alloys with ultra-strength and high-toughness via a property-oriented design strategy L Jiang, C Wang, H Fu, J Shen, Z Zhang, J Xie Journal of Materials Science & Technology 98, 33-43, 2022 | 88 | 2022 |
Effect of quench cooling rate on residual stress, microstructure and mechanical property of an Fe–6.5 Si alloy Z Zhang, W Wang, H Fu, J Xie Materials science and Engineering: A 530, 519-524, 2011 | 82 | 2011 |
Machine learning for materials research and development J XIE, Y SU, D XUE, X Jiang, H Fu, H HUANG Acta Metall Sin 57 (11), 1343-1361, 2021 | 69 | 2021 |
Physical mechanism interpretation of polycrystalline metals’ yield strength via a data-driven method: A novel Hall–Petch relationship L Jiang, H Fu, H Zhang, J Xie Acta Materialia 231, 117868, 2022 | 65 | 2022 |
Effect of electrode materials on AlN-based bipolar and complementary resistive switching C Chen, S Gao, G Tang, H Fu, G Wang, C Song, F Zeng, F Pan ACS Applied Materials & Interfaces 5 (5), 1793-1799, 2013 | 65 | 2013 |
Deformation twinning feature and its effects on significant enhancement of tensile ductility in columnar-grained Fe–6.5 wt.% Si alloy at intermediate temperatures J Xie, H Fu, Z Zhang, Y Jiang Intermetallics 23, 20-26, 2012 | 63 | 2012 |
Progress in materials genome engineering in China Y Su, H Fu, Y BAI, X Jiang, J Xie Acta Metall Sin 56 (10), 1313-1323, 2020 | 62 | 2020 |
A general and transferable deep learning framework for predicting phase formation in materials S Feng, H Fu, H Zhou, Y Wu, Z Lu, H Dong npj Computational Materials 7 (1), 10, 2021 | 61 | 2021 |
Improvement of magnetic properties of an Fe–6.5 wt.% Si alloy by directional solidification H Fu, Z Zhang, Y Jiang, J Xie Materials Letters 65 (9), 1416-1419, 2011 | 55 | 2011 |
Recent progress in the machine learning-assisted rational design of alloys H Fu, H Zhang, C Wang, W Yong, J Xie International Journal of Minerals, Metallurgy and Materials 29 (4), 635-644, 2022 | 46 | 2022 |
The influence of precipitation transformation on Young's modulus and strengthening mechanism of a Cu–Be binary alloy X Huang, G Xie, X Liu, H Fu, L Shao, Z Hao Materials Science and Engineering: A 772, 138592, 2020 | 43 | 2020 |
Strain-softening behavior of an Fe–6.5 wt% Si alloy during warm deformation and its applications H Fu, Z Zhang, Q Yang, J Xie Materials Science and Engineering: A 528 (3), 1391-1395, 2011 | 43 | 2011 |
Effects of precipitated phase and order degree on bending properties of an Fe-6.5 wt% Si alloy with columnar grains H Fu, Q Yang, Z Zhang, J Xie Journal of materials research 26 (14), 1711-1718, 2011 | 42 | 2011 |
Enhanced mechanical properties of polycrystalline Cu–Al–Ni alloy through grain boundary orientation and composition control H Fu, S Song, L Zhuo, Z Zhang, J Xie Materials Science and Engineering: A 650, 218-224, 2016 | 41 | 2016 |
Effects of grain orientation and precipitates on the superelasticity in directionally solidified FeNiCoAlTaB shape memory alloy H Fu, W Li, S Song, Y Jiang, J Xie Journal of Alloys and Compounds 684, 556-563, 2016 | 39 | 2016 |