李伟 - 南京大学 - 化学化工学院

个人简介

李伟，南京大学化学化工学院教授、博士生导师。2002和2007年分别于南京大学强化部(现匡亚明学院)和化学化工学院获学士和博士学位。随后在美国密歇根州立大学化学系依次从事博士后研究和担任研究助理教授。2011-2020年任南京大学化学化工学院副教授，2021年起任南京大学化学化工学院教授、博士生导师。主要从事大分子量子化学方法与程序的发展、以及复杂体系的模拟，已在多种国际重要期刊上发表论文近70篇，撰写国际专著专章4篇，论文总他引1400多次。近五年在国内外重要学术会议上作邀请报告十余次。已主持国家自然科学基金青年基金1项、面上项目3项、教育部博士点基金1项，参与国家自然科学基金重点项目2项等。 2013年获"中国银行奖教金"；2011年获"第一届中国化学会唐敖庆理论化学青年奖"；2019年入选为"创新人才推进计划重点领域创新团队"成员，2019年获教育部自然科学奖一等奖(第二完成人)。

研究领域

量子化学

生物等大分子的机器学习力场大体系的低标度量子化学方法复杂体系的激发态和电子光谱量子化学软件的开发 (LSQC等)

近期论文

查看导师最新文章（温馨提示：请注意重名现象，建议点开原文通过作者单位确认）

Zhu, Q.; Ge, Y.; Li, W.*; Ma, J.* Treating Polarization Effects in Charged and Polar Bio-Molecules Through Variable Electrostatic Parameters. J. Chem. Theory Comput. 2023, 19 (2), 396–411. https://doi.org/10.1021/acs.jctc.2c01130 Hong, B.; Fang, T.; Li, W.*; Li, S.* Predicting the Structures and Vibrational Spectra of Molecular Crystals Containing Large Molecules with the Generalized Energy-Based Fragmentation Approach. J. Chem. Phys. 2023, 158 (4), 044117. https://doi.org/10.1063/5.0137072 Zhao, D.; Liao, K.; Hong, B.; Li, W.*; Li, S.* Accurate and Efficient Prediction of Vibrational Circular Dichroism Spectra of Condensed-Phase Systems with the Generalized Energy-Based Fragmentation Method. Electron. Struct. 2023, 5 (1), 014001. https://doi.org/10.1088/2516-1075/acb1e7 Du, J.; Liao, K.; Ma, J.; Li, W.*; Li, S.* Generalized Energy-Based Fragmentation Approach for the Electronic Emission Spectra of Large Systems. J. Chem. Theory Comput. 2022, 18 (12), 7630–7638. https://doi.org/10.1021/acs.jctc.2c00911 Du, J.; Ma, Y.; Ma, J.; Li, S.; Li, W.* Transition Orbital Projection Approach for Excited State Tracking. J. Chem. Phys. 2022, 156 (21), 214104. https://doi.org/10.1063/5.0081207. Liao, K.; Dong, S.; Cheng, Z.; Li, W.*; Li, S*. Combined Fragment-Based Machine Learning Force Field with Classical Force Field and Its Application in the NMR Calculations of Macromolecules in Solutions. Phys. Chem. Chem. Phys. 2022, 24 (31), 18559-18567. https://doi.org/10.1039/D2CP02192G Cheng, Z.; Du, J.; Zhang, L.; Ma, J.*; Li, W.*; Li, S.* Building Quantum Mechanics Quality Force Fields of Proteins with the Generalized Energy-Based Fragmentation Approach and Machine Learning. Phys. Chem. Chem. Phys. 2022, 24 (3), 1326–1337. (Inside cover) https://doi.org/10.1039/D1CP03934B Zhang, L.; Cheng, Z.; Li, W.*; Li, S.* Generalized Energy-Based Fragmentation Approach for the Accurate Binding Energies and Raman Spectra of Methane Hydrate Clusters. Chin. J. Chem. Phys. 2022, 35 (1), 167–176. https://doi.org/10.1063/1674-0068/cjcp2111256 Wang, Y.; Ni, Z.; Neese, F.; Li, W.; Guo, Y.*; Li, S.* Cluster-in-Molecule Method Combined with the Domain-Based Local Pair Natural Orbital Approach for Electron Correlation Calculations of Periodic Systems. J. Chem. Theory Comput. 2022, 18 (11), 6510–6521. https://doi.org/10.1021/acs.jctc.2c00412. Li, Y.; Wang, D.; Fu, F.; Xia, Q.; Li, W.; Li, S.* Structures and Properties of Ionic Crystals and Condensed Phase Ionic Liquids Predicted with the Generalized Energy‐based Fragmentation Method. J. Comput. Chem. 2022, 43 (10), 704–716. https://doi.org/10.1002/jcc.26828 Li, S.; Li, W.; Jiang, Y.; Ma, J.; Fang, T.; Hua, W.; Hua, S.; Dong, H.; Zhao, D.; Liao, K.; Zou, W.; Ni, Z.; Wang, Y.; Shen, X.; Hong, B. LSQC Program, Version 2.5. Nanjing University, Nanjing 2022. https://itcc.nju.edu.cn/lsqc/ Li, W.; Dong, H.; Ma, J.; Li, S.* Structures and Spectroscopic Properties of Large Molecules and Condensed-Phase Systems Predicted by Generalized Energy-Based Fragmentation Approach. Acc. Chem. Res. 2021, 54 (1), 169–181. https://doi.org/10.1021/acs.accounts.0c00580 Li, W.*; Ma, H.*; Li, S.; Ma, J.* Computational and Data Driven Molecular Material Design Assisted by Low Scaling Quantum Mechanics Calculations and Machine Learning. Chem. Sci. 2021, 12 (45), 14987–15006. https://doi.org/10.1039/D1SC02574K Fu, F.; Liao, K.; Liu, Z.; Hong, D.; Yang, H.; Tian, Y.; Wei, W.; Liu, C.; Li, S.; Ma, J.*; Li, W.* Controlled Fluorescence Enhancement of DNA-Binding Dye Through Chain Length Match between Oligoguanine and TOTO. J. Phys. Chem. B 2021, 125 (2), 518–527. https://doi.org/10.1021/acs.jpcb.0c09611 Liao, K., Wang, S., Li, W.*, & Li, S.* Generalized energy-based fragmentation approach for calculations of solvation energies of large systems. Phys. Chem. Chem. Phys, 2021, 23(35), 19394-19401. https://doi.org/10.1039/D1CP02814F Du, J.; Liao, K.; Hong, B.; Wang, Z.; Ma, J.; Li, W.*; Li, S.* Generalized Energy-Based Fragmentation Clustering Algorithm for Localized Excited States. Chem. J. Chinese Universities, 2021, 42(7), 2227-2237. <https://doi.org/10.7503/cjcu20210314> Ni, Z.; Guo, Y.; Neese, F.; Li, W.; Li, S.* Cluster-in-Molecule Local Correlation Method with an Accurate Distant Pair Correction for Large Systems. J. Chem. Theory Comput. 2021, 17 (2), 756–766. https://doi.org/10.1021/acs.jctc.0c00831 Zhang, L.; Zhu, Q.; Gao, L.; Yang, L.; Li, W.; Li, S.; Zhu, J.*; Wang, W.*; Zeng, G.* Rational Design of the Nickel-Borane Complex for Efficient Hydrogenation of Styrene. J. Comput. Chem. 2021, 42 (8), 545–551. https://doi.org/https://doi.org/10.1002/jcc.26480 Li, S.; Sun, Y.; Wu, C.; Hu, W.; Li, W.; Liu, X.; Chen, M.; Zhu, Y.* Distinct Structure Assembly Driven by Metal–Ligand Binding in Au23 Nanoclusters and Its Relation to Photocatalysis. Chem. Commun. 2021, 57 (17), 2176–2179. https://doi.org/10.1039/D0CC08327E Cheng, Z.; Zhao, D.; Ma, J.; Li, W.*; Li, S.* An On-the-Fly Approach to Construct Generalized Energy-Based Fragmentation Machine Learning Force Fields of Complex Systems. J. Phys. Chem. A 2020, 124 (24), 5007–5014. https://doi.org/10.1021/acs.jpca.0c04526