Welcome to the homepage of Sheng Bi’s Group
We are a computational chemistry group specializing in molecular simulations of energy materials. We are affiliated with the AI4EC Lab at Xiamen University in Xiamen, China.
Our research focuses on the development and application of advanced computational chemistry methods, including Molecular Dynamics, first-principles calculations, and machine-learning approaches. We investigate the structure, dynamics, and properties of complex materials, with a particular interest on designing and optimizing innovative materials for energy storage and conversion applications, such as supercapacitors and batteries.
- Developing AI methods for computational chemistry
- Science agents for autonomous computational chemistry research
- Differentiable molecular dynamics
- Machine learning force fields
- Microscopic modeling of energy storage materials
- Ion storage mechanisms in porous materials
- Structure and dynamics of electrochemical interfaces
- Ion transport and diffusion in condensed electrolytes
We are always looking for talented and motivated students to join our group. If you are interested in pursuing a PhD or Master’s degree in computational chemistry, please feel free to contact Dr. Sheng Bi.
News
April, 2025: I officially joined Xiamen University as an Associate Professor.
Sep, 2025: I was awarded the NSFC Young Scientists Fund (国自然青年项目C).
Dec, 2025: I have been selected for a national-level young talent program (国家级青年人才项目) and received the associated research funding; I have also been appointed as a Nanqiang A Professor at Xiamen University (南强青年拔尖人才A岗).
Jan, 2026: We were granted the National Science and Technology Major Project on New Generation Artificial Intelligence (新一代人工智能国家科技重大专项), in which I serve as a sub-project leader (子课题负责人).
June, 2026: I won first prize in the 2026 Young Faculty Teaching Skills Competition at Xiamen University’s College of Materials (厦门大学材料学院青年教师教学技能比赛一等奖), held on June 18.
July 1, 2026: Our preprint, Ai2-Kit: Streamlining AI-Accelerated Ab Initio Workflows for Complex Chemical Systems, is now available on arXiv.
July 16, 2026: Our preprint, Full-data accuracy with fewer labels for training and fine-tuning machine-learning force fields, is now available on arXiv.
October 11, 2026: Our preprint, Kernel-Level Constant-Potential Molecular Dynamics in GROMACS, is now available on arXiv. The code and examples are available in our GitHub repository.