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个人简介

关于我

  我是李盛洲，已于2025年3月在筑波大学获得情报工学博士学位。我的博士导师是NIMS的中田彩子研究员和筑波大学的樱井铁也教授，博士论文是《Machine Learning for the Prediction and Analysis of Material Electronic Structures》。目前正在浙江大学从事博士后研究。

研究兴趣

KVM Docker PHP Python Nodejs Linux R Mysql Photoshop

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），工学博士（2020年10月~2025年3月）（文部科学省奖学金）

工作经历

  • 国立研究开发法人物质材料研究机构（日本），纳米结构材料中心（MANA），半导体分野，第一性原理量子物性组，博士后研究员（2025年4月~2026年2月）[网页链接]
  • 浙江大学（中国），航空航天学院，应用力学研究所，王杰老师课题组，博士后研究员 (2026年3月~至今) [网页连接]

论文发表

  • (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]
  • Y Xu, S Wu, B Lv, Y Zhu, H Zhang, S Li, D Dai. A study on phase prediction methods based on wavelet transform and deep learning by XRD data. JOM, 2026. [DOI]
  • H Zhang, Y Lin, S Li, M Dai, Y Zhang, L Huang, J Pang, P Wu, J Peng, Z Tang, P Ding, X Wei, N Song, D Dai. Substructure-enhanced MPNN for ploymer discovery and knowledge: a study in predicting glass transition temperature[J], Macromolecules, 2025，58(17), 9515–9527. [DOI]
  • H Zhang, M Dai, Y Lin, B Xu, P Wu, L Huang, H Xu, S Li, Y Xu, Z Tang, J Zhang, R Che, T Xu, D Dai. Subgroup discovery similarity score (SDSS): A significant criterion for the integration of statistical knowledge into machine learning in materials science[J]. Materials Today Physics, 2025, 56, 101772. [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](中文)
  • 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]

联系我

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