Taoyong Cui (崔涛镛)

🌐 Welcome to my personal web page!

I am currently a Ph.D. student at the AI4LS Laboratory and Multimedia Laboratory (MMLab) in The Chinese University of Hong Kong (CUHK), co-supervised by Prof. Pheng Ann Heng and Prof. Wanli Ouyang. I am also a visiting PhD researcher at Yale University, collaborating with Prof. Rex Ying.

Before joining CUHK, I received my Academic Master’s degree in Biomedical Engineering from Tsinghua University. I served as a research intern at Microsoft Research and Shanghai Artificial Intelligence Laboratory, and have served as a reviewer for Nature Communications, AISTATS, ICML, ICLR, NeurIPS, and AAAI.

My research focuses on AI for Science, particularly on machine learning force fields, deep generative models, and large language model applications. You can find more details in my Google Scholar. Please feel free to contact me for communication and collaboration.

📢 News and Highlights

• [November 2025] One paper is accepted by Nature Communications.
• [November 2025] One paper is accepted by Scientific Data.
• [October 2025] One paper is accepted by Current Opinion in Structural Biology.
• [July 2025] One paper is accepted by Advanced Science.
• [February 2025] One paper is accepted by Nature Communications.
• [November 2024] One paper is accepted by VLDB.
• [April 2024] One paper is accepted by Nature Machine Intelligence.

🔍 Selected Research

Evidential Deep Learning for Interatomic Potentials

Han Xu†, Taoyong Cui†, Chenyu Tang1†, Jinzhe Ma, Dongzhan Zhou, Yuqiang Li, Xiang Gao, Xingao Gong, Wanli Ouyang, Shufei Zhang, Mao Su (†These authors contributed equally to this work.)

• Description:In this work, we show an evidential deep learning framework for interatomic potentials with a physics-inspired design. Our method provides uncertainty quantification without significant computational overhead or decreased prediction accuracy, consistently outperforming other methods across a variety of datasets.
• Note: This paper has been accepted at Nature Communications.

Online test-time adaptation for better generalization of interatomic potentials to out-of-distribution data

Taoyong Cui, Chenyu Tang, Dongzhan Zhou, Yuqiang Li, Xingao Gong, Wanli Ouyang, Mao Su, Shufei Zhang

• Description: In this work, we propose an online Test-time Adaptation Interatomic Potential (TAIP) framework to improve the generalization on test data. Specifically, we design a dual-level self-supervised learning approach that leverages global structure and atomic local environment information to align the model with the test data.
• Note: This paper has been accepted at Nature Communications.

Geometry-enhanced pretraining on interatomic potentials

Taoyong Cui, Chenyu Tang, Mao Su, Shufei Zhang, Yuqiang Li, Lei Bai, Yuhan Dong, Xingao Gong, Wanli Ouyang

• Description: In this work, we propose a geometric structure learning framework that leverages unlabelled configurations to improve the performance of MLIPs. Our framework consists of two stages: first, using classical molecular dynamics simulations to generate unlabelled configurations of the target molecular system; and second, applying geometry-enhanced self-supervised learning techniques, including masking, denoising and contrastive learning, to capture structural information.
• Note: This paper has been accepted at Nature Machine Intelligence.
• Honors: The work was awarded 🏅Outstanding Thesis Award for Young Researchers in World Artificial Intelligence Conference 2024 and 🏅Best Paper Award at the Guangdong-Hong Kong-Macao Greater Bay Area AI for Science PhD Forum 2024.

🗣️ Talks

• World Artificial Intelligence Conference (July 2024)
• Guangdong-Hong Kong-Macao Greater Bay Area AI for Science PhD Forum (October 2024)
• China Academic Forum on Interdisciplinary Innovation for Graduate Students in Materials Science (December 2024)
• World Artificial Intelligence Conference (July 2025)
• Guangdong-Hong Kong-Macao Greater Bay Area AI for Science PhD Forum (September 2025)

📬 Contact

• Email: cty21@tsinghua.org.cn