
Tongqi Wen
Research Assistant Professor
Ph.D. in Materials Science and Engineering
Department of Mechanical Engineering
The University of Hong Kong
Research Interests
Introduction
Hello! I'm Tongqi Wen, a lively and driven researcher, passionate about making meaningful contributions to the exciting fields of Artificial Intelligence and Materials Science. Currently, I'm a Research Assistant Professor in the Department of Mechanical Engineering at the University of Hong Kong (HKU). My journey in science has been full of exciting opportunities and collaborations, and I'm always looking forward to the next challenge.
My research combines machine learning with atomistic simulations to explore fascinating materials, including high-entropy alloys, glass, and defect properties. I'm driven by the belief that technology and innovation can create breakthroughs that shape a brighter future, and I'm always excited to push the boundaries of what we know. Let's explore, discover, and innovate together!
Before joining HKU, I had the honor of conducting research at renowned institutions like Ames National Laboratory, Iowa State University (USA), and City University of Hong Kong. Along the way, I was humbled to receive the Ross Coffin Purdy Award from the American Ceramic Society in 2021.
Recent News
View all newsSelected Papers
View all papersSiyu Liu, Jiamin Xu, Beilin Ye, Bo Hu, David J. Srolovitz*, Tongqi Wen*
May 16, 2025
🛠️ How can we evaluate and improve LLMs' capabilities in materials science tools?

Yun Hao, Che Fan, Beilin Ye, Wenhao Lu, Zhen Lu, Peilin Zhao, Zhifeng Gao*, Qingyao Wu*, Yanhui Liu*, Tongqi Wen*
February 25, 2025
🏗 How to efficiently mine high-quality knowledge from literature and apply it to new material discovery?

Zhuoyuan Li, Siyu Liu, Beilin Ye, David J. Srolovitz*, Tongqi Wen*
February 24, 2025
⚛️ Can AI automatically help us discover new materials with given properties?

Bo Hu, Siyu Liu, Beilin Ye, Yun Hao, Tongqi Wen*
November 25, 2024
🚀 Does AI possess the intelligence to autonomously discover materials laws?

Siyu Liu, Tongqi Wen*, Beilin Ye, Zhuoyuan Li, David J. Srolovitz*
May 20, 2025
🧀 Can the materials knowledge stored in LLMs help us predict material properties?

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