Study on the factors affecting solid solubility in binary alloys: An exploration by Machine Learning S Li, H Zhang, D Dai, G Ding, X Wei, Y Guo Journal of Alloys and Compounds 782, 110-118, 2019 | 29 | 2019 |
Prediction of superconducting transition temperature using a machine-learning method Y Liu, H Zhang, Y Xu, S Li, D Dai, C Li, G Ding, W Shen, Q Qian Materiali in tehnologije 52 (5), 639-643, 2018 | 8 | 2018 |
Application of fuzzy learning in the research of binary alloys: Revisit and validation H Zhang, G Zhou, S Li, X Fan, Z Guo, T Xu, Y Xu, X Chen, D Dai, Q Qian Computational Materials Science 172, 109350, 2020 | 7 | 2020 |
Deriving equation from data via knowledge discovery and machine learning: A study of Young’s modulus of Ti-Nb alloys H Zhang, X Liu, G Zhang, Y Zhu, S Li, Q Qian, D Dai, R Che, T Xu Computational Materials Science 228, 112349, 2023 | 2 | 2023 |
An end-to-end machine learning framework exploring phase formation for high entropy alloys H Zhang, R Hu, X Liu, S Li, G Zhang, Q Qian, G Ding, D Dai Transactions of Nonferrous Metals Society of China, 2022 | 2 | 2022 |
A domain knowledge enhanced machine learning method to predict the properties of halide double perovskite A 2 B+ B 3+ X 6 X Wei, Y Zhang, X Liu, J Peng, S Li, R Che, H Zhang Journal of Materials Chemistry A 11 (37), 20193-20205, 2023 | | 2023 |
A New Method to Characterize Limited Material Datasets of High-Entropy Alloys Based on the Feature Engineering and Machine Learning D Dai, T Xu, H Hu, Z Guo, Q Liu, S Li, Q Qian, Y Xu, H Zhang Available at SSRN 3442010, 0 | | |