ChunhuiLi | Molecular Dynamics | Best Researcher Award
Dr. ChunhuiLi , lawrence berkeley national laboratory, United States.
Dr.Chunhui Li, PhDĀ (Ā chunhuili@lbl.govĀ |Ā Ā LinkedInĀ |Ā Ā GitHub) is a post-doctoral fellow at Lawrence Berkeley National Laboratory, specializing in deep learning and molecular dynamics simulations. Dr. Li’s research focuses on developing machine learning models for rapid predictions in geoscience and battery applications, with significant speed improvements over conventional methods.
Publication Profile
Education and Experience
Dr.Chunhui Li earned a Doctor of Philosophy in Mechanical Engineering ( Washington State University, July 2019) with a dissertation on simulation studies of protein-particle interactions in battery applications, advised by Prof. Jin Liu. He also holds a Master of Engineering in Mechanical Engineering ( Harbin Institute of Technology, July 2013) with a thesis on grinding trajectories of SiC Aspherical, advised by Prof. Feihu Zhang, and a Bachelor of Engineering in Manufacturing Engineering ( Harbin Institute of Technology, July 2011). Since July 2020, Dr. Li has been working at Lawrence Berkeley National Laboratory, focusing on rapid prediction of liquid structure using deep learning and developing machine learning surrogates for Geoscience Models.
Professional Development
Dr.Chunhui Li has honed technical skills in molecular dynamics simulationĀ (Ā GROMACS, LAMMPS, VASP),Ā machine learning/deep learning/data scienceĀ (Ā PyTorch, TensorFlow, Scikit-Learn, Pandas, Numpy), andĀ programming languagesĀ (Ā Python, Java, C/C++, Shell script, MATLAB, Perl). He has designed and managed high-throughput molecular dynamics simulations, curated extensive datasets, and developed efficient AI models, showcasing impressive speed improvements over traditional methods.
Research Focus
Dr. Li’s research focuses on the application ofĀ machine learningĀ andĀ molecular dynamicsĀ to geoscience and battery applications. His notable work includes developing deep learning models for rapid prediction of time-averaged structural properties, creating AI-based surrogates for geochemistry models, and improving the ion conductivity of solid polymer electrolytes through protein-coated nanofillers. Dr.Li’s collaborative efforts with experimental researchers have led to significant advancements in computational physics and material science.
PublicationsĀ
- Self-Assembled Protein Nanofilter for Trapping Polysulfides and Promoting Li+ Transport in LithiumāSulfur Batteries X Fu, C Li, Y Wang, L Scudiero, J Liu, WH Zhong The journal of physical chemistry letters 9 (10), 2450-2459 41 2018
- Let It Catch: A ShortāBranched Protein for Efficiently Capturing Polysulfides in LithiumāSulfur Batteries M Chen, C Li, X Fu, W Wei, X Fan, A Hattori, Z Chen, J Liu, WH Zhong Advanced Energy Materials 10 (9), 1903642 38 2020
- Building Ion-Conduction Highways in Polymeric Electrolytes by Manipulating Protein Configuration X Fu, C Li, Y Wang, LP Kovatch, L Scudiero, J Liu, W Zhong ACS Applied Materials & Interfaces 10 (5), 4726-4736 37 2018
- Unseeded, Spontaneous Nucleation of Spherulitic Magnesium Calcite M Prus, C Li, K KÄdra-KrĆ³lik, W Piasecki, K Lament, T BegoviÄ, P Zarzycki Journal of Colloid and Interface Science 593, 359-369 15 2021
- Dissipative Particle Dynamics Simulations of a Protein-Directed Self-Assembly of Nanoparticles C Li, X Fu, W Zhong, J Liu ACS Omega 4 (6), 10216-10224 15 2019
- Machine Learning in Heterogeneous Porous Materials M D’Elia, H Deng, C Fraces, K Garikipati, L Graham-Brady, A Howard, … arXiv preprint arXiv:2202.04137 7 2022
- A Computational Pipeline to Generate a Synthetic Dataset of Metal Ion Sorption to Oxides for AI/ML Exploration C Li, P Zarzycki Frontiers in Nuclear Engineering 1, 977743 6 2022