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About me

  I’m Li Shengzhou. Nowadays, I am a PhD student of Computer Science in University of Tsukuba. My research topic is “Data-Driven and Machine Learning Based Material Science Research” under the supervision of Pro. Nakata Ayako from NIMS and Pro. Sakurai Tetsuya from University of Tsukuba.


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  • Shanghai University (China), School of Computer Engineering and Science, Bachelor degree. (2012/09~2016/06)
  • Shanghai University (China), School of Computer Engineering and Science, Master degree. (2016/09~2019/04)
  • Northeast Normal University (China), Learning Japanese. (2019/10~2020/08)
  • University of Tsukuba (Japan), Graduate School of Science and Technology, Degree Programs in Systems and Information Engineering, Doctoral Program in Computer Science. (2020/10~Now) (MEXT Scholarship)


  • S Li, A Nakata. CSIML: a cost-sensitive and iterative machine-learning method for small and imbalanced materials data sets[J]. Chemistry Letters, 2024, 53(5).[DOI]
  • S Li, H Zhang, D Dai, G Ding, X Wei, Y Guo. Study on the factors affecting solid solubility in binary alloys: An exploration by Machine Learning[J]. Journal of Alloys and Compounds, 2019, 782: 110-118.[DOI]
  • Wei X, Zhang Y, Liu X, J Peng, S Li, R Che, H Zhang. A domain knowledge enhanced machine learning method to predict the properties of halide double perovskite \(A_2B^+B^{3+}X_6\) [J]. Journal of Materials Chemistry A, 2023.[DOI]
  • H Zhang, X Liu, G Zhang, Y Zhu, S Li, Q Qian, D Dai, R Che, T Xu, Deriving equation from data via knowledge discovery and machine learning: A study of Young’s modulus of Ti-Nb alloys[J]. Computational Materials Science, 2023, 228:112349.[DOI]
  • H Zhang, R Hu, X Liu, S Li, G Zhang, Q Qian, G Ding, D Dai. An end-to-end machine learning framework exploring phase formation for high entropy alloys[J]. Transactions of Nonferrous Metals Society of China, 2022, [DOI]
  • W Zheng , H Zhang, H Hu, Y Liu, S Li, G Ding, J Zhang. Performance prediction of perovskite materials based on different machine learning algorithms[J]. The Chinese Journal of Nonferrous Metals, 2019, 29(04): 803-809.[DOI](Chinese)
  • Y Liu, H Zhang, Y Xu, S Li, D Dai, C Li, G Ding, W Shen, Q Qian. Prediction of Superconducting Transition Temperature Using A Machine-Learning Method[J]. Materiali in tehnologije, 2018, 52(5): 639-643.[DOI]
  • H Zhang, G Zhou, S Li, X Fan, Z Guo, T Xu, Y Xu, X Chen, D Dai, Q Qian. Application of fuzzy learning in the research of binary alloys: Revisit and validation[J]. Computational Materials Science, 2020, 172: 109350.[DOI]
  • D Dai, T Xu, H Hu, Z Guo, Q Liu, S Li, Q Qian, Y Xu, H Zhang. A New Method to Characterize Limited Material Datasets of High-Entropy Alloys Based on the Feature Engineering and Machine Learning[J]. Available at SSRN 3442010.[DOI]


Email: zhonger[at] (Please replace [at] with @.)