● Artificial intelligence assisted energy material structure design;
● Artificial Intelligence assisted Biomaterials and Big Data (Bioinformatics)

● Ph.D. 2007/09-2012/06, Department of physics and astronomy, Shanghai Jiao Tong University, China.
● B.S. 2003/09-2007/06, Department of physics, Tianjin University of Technology, China.

● 2015/12-now: Professor, Shanghai Jiao Tong University, Shanghai, China.
● 2014/07-2015/12: Senior Researcher, University of California, Santa Barbara USA.
● 2012/07-2014/06: Postdoc, University of Illinois at Urbana-Champaign, USA.

● Artificial intelligence assisted energy material structure design
In view of the shortcomings of traditional experimental designs, we developed artificial intelligence-assisted methods to explore new energy conversion and storage materials. We designed and revealed the intrinsic physical and chemical properties of materials to provide a theoretical basis for experimental preparations and future applications. With the continuous improvement of computational simulations and the performance of supercomputers, the basic properties of materials and their phenomena in application can be simulated at the atomic scale. Artificial intelligence-assisted methods can shorten the design and screening time of novel energy systems. Our goal is to discover high-performance energy materials and reveal the underlying physical mechanisms using first-principles calculations and machine learning methods.

● Artificial Intelligence assisted Biomaterials and Big Data (Bioinformatics)
Our research focuses on the stability, catalytic activity and conformation relationships of proteins and biomolecular materials such as DNA/RNA in industrial environments. The team developed a "quantum fragmentation" algorithm based on the potential energy surface of deep neural network to reveal the configuration relations of biomolecular materials and realize the structural design, discovery and application. Using machine learning methods, the team has achieved many results in novel Coronavirus molecules, several enzyme molecules and medical big data diagnosis and treatment.

●1. New micro/nano materials and theoretical design (Graduate course)
●2. Lithium-ion batteries and supercapacitors (Graduate course)
●3. Bioinformatics (Undergraduate course)

Selected publications:
1.Yanqiang Han, Imran Ali, Zhilong Wang, Junfei Cai, Sicheng Wu, Jiequn Tang, Lin Zhang, Jiahao Ren, Rui Xiao, Qianqian Lu, Lei Hang, Hongyuan Luo and Jinjin Li*, Machine Learning Accelerates Quantum Mechanics Predictions of Molecular Crystals. Physics Reports 934, 1-71 (2021). (IF=25.6)
2.Sicheng Wu, Zhilong Wang, Kaikuo Zhang, Junfei Cai, and Jinjin Li*, Deep Learning Accelerates the Discovery of Two-Dimensional Catalysts for Hydrogen Evolution Reaction. Energy & Environmental Materials (2021). https://doi.org/10.1002/eem2.12259
3.Haikuo Zhang, Zhilong Wang, Junfei Cai, Sicheng Wu and Jinjin Li*, Machine Learning Enabled Tricks of the Trade for Rapid Host Material Discovery in Li-S Battery. ACS Applied Materials & Interfaces (2021). https://doi.org/10.1021/acsami.1c10749
4.Junfei Cai, Zhilong Wang, Sicheng Wu, Yanqiang Han, and Jinjin Li*, A Machine Learning Shortcut for Screening the Spinel Structures of Mg/Zn Ion Battery Cathodes with a High Conductivity and Rapid Ion Kinetics. Energy Storage Materials 42, 277–285 (2021).
5.Zhilong Wang, Junfei Cai, Qingxun Wang, Sicheng Wu, Jinjin Li*, Unsupervised Discovery of Thin-Film Photovoltaic Materials from Unlabeled Data. npj Computational Materials 7, 128 (2021).
6.Zhilong Wang, Xirong Lin, Yanqiang Han, Junfei Cai, SichengWu, Xing Yu, and Jinjin Li*, Harnessing Artificial Intelligence to Holistic Design and Identification for Solid Electrolytes. Nano Energy 89, 106337 (2021).
7.Zhilong Wang, Qingxun Wang, Yanqiang Han, Yan Ma, Hua Zhao, Andrzej Nowak, and Jinjin Li*, Deep Learning for Ultra-fast and High Precision Screening of Energy Materials. Energy Storage Materials 39, 45-53 (2021).  
8.Yanqiang Han, Zhilong Wang, Zhiyun Wei, Jinyun Liu, and Jinjin Li*, Machine Learning Builds Full-QM Precision Protein Force Fields in Seconds. Briefings in Bioinformatics (2021). bbab158, https://doi.org/10.1093/bib/bbab158
9.Zhilong Wang, Haikuo Zhang, Jinjin Li*, Accelerated Discovery of Stable Spinels in Energy Systems via Machine Learning. Nano Energy 81, 105665 (2021).
10.Zhilong Wang, Haikuo Zhang, Jiahao Ren, Xirong Lin, Tianli Han, Jinyun Liu, Jinjin Li*, Predicting Adsorption Ability of Adsorbents at Arbitrary Sites for Pollutants using Deep Transfer Learning. npj Computational Materials 7, 19 (2021).
11.Yanqiang Han, Zhilong Wang, Jinjin Li*, Neural Networks Accelerate the Ab initio Prediction of Solid-Solid Phase Transitions at High Pressures. Journal of Physical Chemistry Letters 12, 132-137 (2021).
12.Haikuo Zhang, Zhilong Wang, Jinyun Liu, Jinjin Li*, Ultra-fast and Accurate Binding Energy Prediction of Shuttle Effect-Suppressive Sulfur Hosts for Lithium-Sulfur Batteries Using Machine Learning. Energy Storage Materials 35, 88-98 (2021).
14.Xirong Lin, Jinyun Liu, Haikuo Zhang, Yan Zhong, Mengfei Zhu, Ting Zhou, Xue Qiao, Huigang Zhang, Tianli Han, and Jinjin Li*, General Liquid-Driven Coaxial Flow Focusing Preparation of Novel Microcapsules for Rechargeable Magnesium Batteries. Advanced Science 8, 2002298 (2020).
15.Lin Zhang, Zhilong Wang, Zhiyun Wei, Jinjin Li*, Unsupervised Assisted Directional Design of Chemical Reactions. Cell Reports Physical Science 1, 100269 (2020).
16.Jiahao Ren, Xirong Lin, Jinyun Liu, Tianli Han, Zhilong Wang, Haikuo Zhang and Jinjin Li*, Engineering Early Prediction of Supercapacitors' Cycle Life using Neural Networks. Materials Today Energy 18, 100537 (2020).
17. Jinjin Li*, Carl J. Tilbury, Seung Ha Kim, Michael F. Doherty, A design aid for crystal growth engineering. Progress in Materials Science 82, 1-38 (2016) (IF=32)

The group is currently recruiting new graduate students. Please contact Prof. Li (lijinjin@sjtu.edu.cn) for further information.