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Li Shengzhou
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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
292019
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
82018
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
72020
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
22023
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
22022
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
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