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 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
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
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
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
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
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
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
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
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.
Machine Learning Chemical Reactions
Machine learning methods, especially deep learning, have significantly expanded a chemist's toolbox, enabling the construction of quantitatively predictive models directly from data. These models make it possible to explore the gigantic chemical space to make chemical discoveries.
Our group focus on the chemical reaction space. We develop novel graph neural networks that are able to represent any chemical reactions with bond alterations and apply them to model reaction properties such as reaction energy, reaction type, and activation energy. These models have been used to investigate the reaction pathways in battery electrolytes.
Accelerated Atomistic Molecular Simulations
Molecular simulations are a powerful computational technique for exploring material behavior and properties based on an understanding of the physics of bonding at the atomic scale. At the core of any molecular simulation lies a description of the interactions between atoms that produces the forces governing the atomic motion. In classical molecular simulations, such interactions are modeled via interatomic potentials, which make it possible to simulate a large number of atoms for extended periods of time.
We develop both physics-based and machine learning interatomic potentials and apply them to study the energetic, structural, thermal, and mechanical properties of materials and devices for renewable energy applications. We also develop model analysis frameworks (e.g. uncertainty quantification methods), aiming at making classical molecular simulations more reliable, reproducible, and accessible.
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 recipes for density functional theory (DFT) and apply them to discover new materials with unique mechanical and photonic properties with applications in solid-state batteries and thermal-photonic devices.
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
There is currently no postdoc opening.
We take a couple of new graduate students each year. Prospective students are encouraged to take a look at the department website for information on how to apply for admission. Students who have already been admitted please send Dr. Wen an email if you are interested in joining the group.
We welcome undergraduate students to join our group to conduct research. Please contact us if you are interested.
