Welcome to the Wen Group at the University of Electronic Science and Technology of China!
We push the boundaries of artificial intelligence and data-driven computational methods to innovate discovery in materials and chemical sciences.
欢迎访问电子科技大学基础与前沿研究院「文明健课题组」！
Mingjian Wen (文明健)
Professor, Institute of Fundamental and Frontier Sciences, UESTC
mjwen@uestc.edu.cn
Mingjian tracks how atoms move and interact with each other on weekdays and cheers for soccer games on weekends. He seems to be tied to spherical objects.
You
Come and join us.
Wei-Fan Huang
whuang25@uh.edu
Wei-Fan received his B.S. and M.S. in Chemical Engineering from National Cheng Kung University in Taiwan in 2018 and 2021. His research focuses on developing machine learning models and their applications in molecular simulation of battery materials.
Emmanuel A. Olanrewaju
eolanrew@uh.edu
Emmanuel loves computational work to understand and design materials for renewable energy applications. He currently works on data-driven understanding of the stability of zeolites.
Qun Chen (陈群)
qunchen@std.uestc.edu.cn
Qun received her M.S. from Shanghai University. Her research focuses on applying density functional theory and developing machine learning methods to study magnetic materials.
Jiahui Pan (潘佳荟)
202521210106@std.uestc.edu.cn
Jiahui is dedicated to applying molecular dynamics to study battery materials, aiming to deepen the understanding of electrochemical processes and improve battery performance.
Haoyang Wang (汪浩洋)
202522020823@std.uestc.edu.cn
Haoyang holds a B.S. in Space Physics from the University of Science and Technology of China. His research focuses on the application of first-principles theory to study and design magnetic materials.
Qiyao Gao (高启尧)
qiyao_gao@std.uestc.edu.cn
Qiyao majors in Electronic Information Engineering at UESTC. His research interest lies in applying deep learning to materials science.
Boyu Wang (王博宇)
bywang@std.uestc.edu.cn
Boyu is currently pursuing a B.S. at UESTC. His research focuses on the development of machine learning models and their applications in materials science.
Linda Li (李林达)
2024020901017@std.uestc.edu.cn
A UESTCer from Northeast China. If you are patient, warmhearted, and make occasional mistakes (just like me), then we can be friends.
Yaqi Song (宋亚琪)
2024270903001@std.uestc.edu.cn
Yaqi is an undergraduate student in the Honors College at UESTC. With a strong interest in AI, she is eager to contribute to innovative research in the field.
Jiayuan Sun (孙嘉远)
2024270902013@std.uestc.edu.cn
Jiayuan is a sophomore interested in interdisciplinary applications of AI in scientific research. He hopes to gain practical experience and contribute to the development of relevant research projects.
AI for Materials Science
Machine learning methods, particularly deep learning, have significantly expanded our toolbox for constructing predictive and generative scientific models. These models make it possible to accurately and efficiently explore the gigantic design space of new materials and chemicals.
Our group pioneers the development of novel AI algorithms that integrate core physical principles to create such models and advance their capabilities. These algorithms enable both the precise forecasting of material properties and the intelligent design of new compounds with targeted functions.
Accelerated Reliable Atomistic Simulations
Atomistic simulations are a powerful computational technique for exploring material behavior at the atomic scale. At the core of any atomistic simulation lies a description of the interactions between atoms that produces the forces governing their motion.
We develop both physics-based and machine learning interatomic potentials to model these interactions. To ensure the robustness of these models, we also develop analysis frameworks, such as those for uncertainty quantification and propagation, to make atomistic simulations more reliable, reproducible, and accessible.
Energy Storage Systems
Renewable energy storage is essential for building a sustainable future, with advanced batteries playing a critical role in enabling reliable grid integration of solar and wind power. Our research develops the fundamental science needed for next-generation, grid-scale storage systems.
We apply theoretical and multiscale computational methods to investigate key challenges in battery systems, including ion transport mechanisms, interface phenomena, and degradation processes across multiple scales.
High-throughput Materials Discovery
Quantum chemical theory and computations are powerful tools for understanding and designing materials. Conventional approaches that manually perform the calculations are difficult to manage complex materials science workflows and difficult to fully utilize modern high-performance computing resources.
Building on top of the software stack that powers the Materials Project, we develop high-throughput materials discovery pipelines for density functional theory (DFT) and machine learning techniques, and apply them to search for materials for renewable energy and information applications.
MatTen
An equivariant graph neural model for predicting tensorial properties of crystals such as the elasticity tensor.
GitHub
KLIFF
A fitting package that can be used to develop both physics-based and machine learning interatomic potentials.
GitHub
BonDNet
A graph neural network machine learning model for the prediction of bond dissociation energies.
GitHub
RxnRep
Contrastive pretraining to learn chemical reaction representations (RxnRep) for downstream tasks.
GitHub
We are looking for motivated postdocs in the areas of AI for Science and Ferroic Materials. For more information, see
Our group welcomes new PhD and Master students from diverse academic backgrounds each year. We value interdisciplinary collaboration and encourage students with backgrounds in Materials Science, Computer Science, Physics, Chemistry, Solid Mechanics, and other related fields to apply.
We welcome undergraduate students to join our group to conduct research. Please contact us if you are interested.
