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Introduction

About me

  I’m Li Shengzhou and obtained the PhD degree for Computer Science from University of Tsukuba in March 2025. My PhD thesis is “Machine Learning for the Prediction and Analysis of Material Electronic Structures” which is supervised by Pro. Nakata Ayako from NIMS and Pro. Sakurai Tetsuya from University of Tsukuba. From April 2025, I start my work in Nationa Institute for Materials Science (NIMS) as a NIMS Postdoctor Researcher.

Interests

KVM Docker PHP Python Nodejs Linux R Mysql Photoshop

Educations

  • 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 degree. (2020/10~2025/03) (MEXT Scholarship)

Work

  • National Institute for Materials Science (Japan), Research Center for Materials Nanoarchtectonics (MANA), Semiconductor Materials Field, Quantum Materials Group, NIMS Postdoctor Researcher (2025/04~Now) [Website]

Publications

  • (Cover Paper) S Li, T Miyazaki, A Nakata. Theoretical search for characteristic atoms in supported gold nanoparticles: a large-scale DFT study[J]. Physical Chemistry Chemical Physics, 2024, 26: 20251-20260 [DOI]
  • 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]
  • D Dai, G Zhang, X Wei, Y Lin, M Dai, J Peng, N Song, Z Tang, S Li, J Liu, Y Xu, R Che, H Zhang. A GPT-assisted iterative method for extracting domain knowledge from a large volume of literature of electromagnetic wave absorbing materials with limited manually annotated data[J]. Computational Materials Science, 2025, 246: 113431.[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]

Contact

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