李敏杰，副教授

邮箱：minjieli@shu.edu.cn

电话：021-66133513

研究领域：分子和材料设计与性能优化

上海市浦江人才计划获得者，从事分子和材料设计与性能优化研究，主持国家级、省部级等项目7项，出版专著3部，发表相关SCI论文50余篇，总引用超过500次。

教育经历：

2002.09-2007.07 中国科学技术大学，有机化学专业，理学博士，导师：郭庆祥教授

1998.09-2002.07 河南师范大学，应用化学

工作经历：

2007.08-至今 yl6809永利化学系，副教授

2019.03-2020.07 多伦多大学化学系，访问教授，合作教授：Alán Aspuru-Guzik

近3年代表性科研项目：

1. 上海市浦江人才计划，高效有机-无机杂化钙钛矿材料的逆向设计，30万，2021.10-2023.09，主持

2. 云南省科技处，云南省稀贵金属材料基因工程项目-锡铟材料基因工程专用数据库平台建设及示范，2020.01-2022.12，2200万，骨干参与

3. 国家科技部重点研发计划课题，材料大数据挖掘和分析技术，2100万，2016.07-2021.03，骨干参与

4. 上海市自然科学基金，光电转化效率高的无铅钙钛矿太阳能电池材料的理性设计与实验验证，30万，2016.07-2019.06，主持

近2年代表性学术论文：

1. Chen Yang; Chang Ren; Yuefei Jia; Gang Wang; Minjie Li*; Wencong Lu*; A machine learning-based alloy design system to facilitate the rational design of high entropy alloys with enhanced hardness, Acta Materialia, 2022, 222.

2. Lu Tian; Li Hongyu; Li Minjie; Wang Shenghao*; Lu Wencong*; Predicting Experimental Formability of Hybrid Organic−Inorganic Perovskites via Imbalanced Learning, Journal of Physical Chemistry Letters, 2022, 13, 3032−3038

3. Liu Xiujuan; Shao Yueyue; Lu Tian; Chang Dongping; Minjie Li*; Wencong Lu*; Materials and Design, 2022, 216, 110561.

4. Zhengheng Lian; Minjie Li*; Wencong Lu*; Fatigue life prediction of aluminum alloy via knowledge-based machine learning , International Journal of Fatigue, 2022, 157.

5. Xu Pengcheng; Chen Huimin; Li Minjie*; Wencong Lu*; New Opportunity: Machine Learning for Polymer Materials

Design and Discovery. Advanced. Theory Simulation, 2022, 2100565.

6. Qiuling Tao; Pengcheng Xu; Minjie Li*; Wencong Lu*; Machine learning for perovskite materials design and discovery, npj Computational Materials, 2021, 7(23). (高倍引论文)

7. Qiuling Tao; Tian Lu; Long Li; Wencong Lu*; Minjie Li*; Machine Learning Aided Design of Perovskite Oxide Materials for Photocatalytic Water Splitting, Journal of Energy Chemistry, 2021, 60: 351-359. (热点论文)

8. Zhang, Shilin; Lu, Tian; Xu, Pengcheng; Tao, Qiuling; Li, Minjie*; Lu, Wencong* ; Predicting the Formability of Hybrid Organic-Inorganic Perovskites via an Interpretable Machine Learning Strategy, Journal of Physical Chemistry Letters, 2021, 12(31): 7423-7430

9. Su, Tianhao; Cui, Yaning; Lian, Zhengheng; Hu, Minglang; Li, Minjie*; Lu, Wencong*; Ren, Wei*; Physics-Based Feature Makes Machine Learning Cognizing Crystal Properties Simple, Journal of Physical Chemistry Letters, 2021, 12(35): 8521-8527.

10. Tao, Qiuling; Chang, Dongping; Lu, Tian; Li, Long; Chen, Huimin; Yang, Xue; Liu, Xiujuan; Li, Minjie*; Lu, Wencong*; Multiobjective Stepwise Design Strategy-Assisted Design of High-Performance Perovskite Oxide Photocatalysts. Journal of Physical Chemistry C 2021, 125 (38), 21141-21150.

11. Xu, Pengcheng; Chang, Dongping; Lu, Tian; Li, Long; Li, Minjie*; Lu, Wencong*; Search for ABO3 Type Ferroelectric Perovskites with Targeted Multi-Properties by Machine Learning Strategies. Journal of chemical information and modeling, 2021: 10.1021/acs.jcim.1c00566.

12. Lu, Tian; Li, Minjie*; Yao, Zhenpeng*; Lu, Wencong*; Accelerated discovery of boron-dipyrromethene sensitizer for solar cells by integrating data mining and first principle. Journal of Materiomics, 2021, 7 (4), 790-801.

（最后更新日期：2022.05.09）