Profile

Tongqi Wen

Research Assistant Professor
Ph.D. in Materials Science and Engineering
Department of Mechanical Engineering
The University of Hong Kong

Research Interests

AI for ScienceMachine Learning PotentialsAtomistic SimulationsHigh-entropy Materials
Liquid and Glass

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

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Selected Papers

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MatTools: Benchmarking Large Language Models for Materials Science Tools

Siyu Liu, Jiamin Xu, Beilin Ye, Bo Hu, David J. Srolovitz*, Tongqi Wen*

May 16, 2025

arXiv
AI4SMaterials Tools

🛠️ How can we evaluate and improve LLMs' capabilities in materials science tools?

Illustration for MatTools: Benchmarking Large Language Models for Materials Science Tools
Inverse Materials Design by Large Language Model-Assisted Generative Framework

Yun Hao, Che Fan, Beilin Ye, Wenhao Lu, Zhen Lu, Peilin Zhao, Zhifeng Gao*, Qingyao Wu*, Yanhui Liu*, Tongqi Wen*

February 25, 2025

arXiv
AI4SMaterials Inverse Design

🏗 How to efficiently mine high-quality knowledge from literature and apply it to new material discovery?

Illustration for Inverse Materials Design by Large Language Model-Assisted Generative Framework
Active Learning for Conditional Inverse Design with Crystal Generation and Foundation Atomic Models

Zhuoyuan Li, Siyu Liu, Beilin Ye, David J. Srolovitz*, Tongqi Wen*

February 24, 2025

arXiv
AI4SMaterials Inverse DesignAtomic Models

⚛️ Can AI automatically help us discover new materials with given properties?

Illustration for Active Learning for Conditional Inverse Design with Crystal Generation and Foundation Atomic Models
A Multi-agent Framework for Materials Laws Discovery

Bo Hu, Siyu Liu, Beilin Ye, Yun Hao, Tongqi Wen*

November 25, 2024

arXiv
AI4SMaterials Laws

🚀 Does AI possess the intelligence to autonomously discover materials laws?

Illustration for A Multi-agent Framework for Materials Laws Discovery
Large Language Models for Material Property Predictions: elastic constant tensor prediction and materials design

Siyu Liu, Tongqi Wen*, Beilin Ye, Zhuoyuan Li, David J. Srolovitz*

May 20, 2025

Digital Discovery
AI4SMaterials Properties

🧀 Can the materials knowledge stored in LLMs help us predict material properties?

Illustration for Large Language Models for Material Property Predictions: elastic constant tensor prediction and materials design

Join Us

We are looking for passionate and motivated individuals to join our team! If you are interested in contributing to cutting-edge research in AI and Materials Science, feel free to reach out.