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.

Interests

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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 Program in Computer Science. (2020/10~Now) (MEXT Scholarship)

Publications

  1. 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]
  2. 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)
  3. 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]
  4. 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]
  5. 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 @.)

关于我

  我是李盛洲，目前我正在筑波大学攻读计算机博士学位。我的导师是NIMS的中田彩子研究员和筑波大学的樱井铁也教授，我的主要研究方向是《基于数据驱动和机器学习的材料科学研究》。

研究兴趣

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教育经历

  • 上海大学（中国），计算机工程与科学学院，工学学士（2012年9月~2016年6月）
  • 上海大学（中国），计算机工程与科学学院，工学硕士（2016年9月~2019年4月）
  • 东北师范大学（中国），留日预备学校，日语学习（2019年10月~2020年8月）
  • 筑波大学（日本），情报工学部（计算机科学），博士在读（2020年10月~至今）（文部科学省奖学金）

论文发表

  1. 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]
  2. 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](中文)
  3. 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]
  4. 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]
  5. 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]

联系我

邮箱：zhonger[at]live.cn (请使用@替换[at])