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Topics: Generative Models & Optimal Transport / Stochatic Control & Sampling / Equivariant Neural Networks / Large Language Models / Molecular Discovery / AI for Science (* denotes equal contribution, † denotes advising role)

No Trick, No Treat: Pursuits and Challenges Towards Simulation-free Training of Neural Samplers
Jiajun He*, Yuanqi Du*, Francisco Vargas, ..., Carla P. Gomes, José Miguel Hernández-Lobato
arXiv preprint arXiv:2502.06685 | paper

Efficient Evolutionary Search over Chemical Space with Large Language Models
Haorui Wang*, Marta Skreta*, …, Yuanqi Du†, Alán Aspuru-Guzik†, Kirill Neklyudov†, Chao Zhang†
ICLR 2025 | paper

Diffusion Models as Constrained Samplers for Optimization with Unknown Constraints
Yuanqi Du*, Lingkai Kong*, Wenhao Mu*, Kirill Neklyudov, Valentin De Bortol, ..., Yi-An Ma, Carla P. Gomes, Chao Zhang
AISTATS 2025 | paper

React-OT: Optimal Transport for Generating Transition State in Chemical Reactions
Chenru Duan*, Guan-Horng Liu*, Yuanqi Du*, ..., Carla P. Gomes, Evangelos A. Theodorou, Heather J. Kulik
Nature Machine Intelligence 2025 | paper

Large Language Models Are Innate Crystal Structure Generators
Jingru Gan, Peichen Zhong*, Yuanqi Du*, ..., Carla P. Gomes, Kristin A. Persson, Daniel Schwalbe-Koda, Wei Wang
arXiv preprint | paper

AlphaNet: Scaling Up Local Frame-based Atomistic Foundation Model
Bangchen Yin, Jiaao Wang†, …, Yuanqi Du†, Carla P. Gomes, Chenru Duan†, Hai Xiao†, Graeme Henkelman†
arXiv preprint arXiv:2501.07155 | paper

Doob's Lagrangian: A Sample-Efficient Variational Approach to Transition Path Sampling
Yuanqi Du*, Michael Plainer*, Rob Brekelmans*, ..., Carla P. Gomes, Alán Aspuru-Guzik, Kirill Neklyudov
NeurIPS 2024 (Spotlight) | paper

Navigating Chemical Space with Latent Flows
Guanghao Wei*, Yining Huang*, Chenru Duan, Yue Song†, Yuanqi Du†
NeurIPS 2024 | paper

Machine Learning-Aided Generative Molecular Design
Yuanqi Du*, Arian R. Jamasb*, Jeff Guo*, ..., Pietro Lio, Philippe Schwaller, Tom L. Blundell
Nature Machine Intelligence 2024 | paper

Structure-based Drug Design with Equivariant Diffusion Models
Arne Schneuing*, Charles Harris*, Yuanqi Du*, ..., Carla P. Gomes, Tom Blundell, Pietro Lió, Max Welling, Michael Bronstein, Bruno Correia
Nature Computational Science 2024 | paper

Molecular Contrastive Pretraining with Collaborative Featurizations
Yanqiao Zhu*, Dingshuo Chen*, Yuanqi Du*, Yingze Wang, Qiang Liu, Shu Wu
NeurIPS 2024 | paper

Aligning Large Language Models with Representation Editing: A Control Perspective
Lingkai Kong, Haorui Wang, Wenhao Mu, Yuanqi Du, ..., Chao Zhang
NeurIPS 2024 | paper

Large Language Models are Catalyzing Chemistry Education
Yuanqi Du*, Chenru Duan*, Andres Bran*, ..., Heather Kulik, Antoine Bosselut, Jinjia Xu, Philippe Schwaller
ChemRxiv 10.26434/chemrxiv-2024-h722v | paper

Accurate Transition State Generation with an Object-aware Equivariant Elementary Reaction Diffusion Model
Chenru Duan, Yuanqi Du, Haojun Jia, Heather J. Kulik
Nature Computational Science 2023 (Cover Story) | paper

Scientific Discovery in the Age of Artificial Intelligence
Hanchen Wang*, Tianfan Fu*, Yuanqi Du*, ..., Max Welling, Linfeng Zhang, Connor Coley, Yoshua Bengio, Marinka Zitnik
Nature 2023 | paper

A New Perspective on Building Efficient and Expressive 3D Equivariant Graph Neural Networks
Weitao Du*, Yuanqi Du*, Limei Wang*, Dieqiao Feng, Guifeng Wang, Shuiwang Ji, Carla P. Gomes, Zhi-Ming Ma
NeurIPS 2023 | paper

Path Integral Stochastic Optimal Control for Sampling Transition Paths
Lars Holdijk*, Yuanqi Du*, Priyank Jaini, Ferry Hooft, Bernd Ensing, Max Welling
NeurIPS 2023 | paper

M²Hub: Unlocking the Potential of Machine Learning for Materials Discovery
Yuanqi Du*, Yingheng Wang*, Yining Huang, ..., Tian Xie, Chenru Duan, John M. Gregoire, Carla P. Gomes
NeurIPS 2023 | paper

Uncovering Neural Scaling Law in Molecular Representation Learning
Dingshuo Chen, Yanqiao Zhu, Jieyu Zhang, Yuanqi Du, Zhixun Li, Qiang Liu, Shu Wu, Liang Wang
NeurIPS 2023 | paper

A Flexible Diffusion Model
Weitao Du, Tao Yang, He Zhang, Yuanqi Du
ICML 2023 | paper

ChemSpacE: Interpretable and Interactive Chemical Space Exploration
Yuanqi Du, Xian Liu, Shengchao Liu, Jieyu Zhang, Bolei Zhou
TMLR 2023 | paper

A Systematic Survey of Chemical Pre-trained Models
Jun Xia*, Yanqiao Zhu*, Yuanqi Du*, Stan Z.Li
IJCAI 2023 | paper

Artificial intelligence for science in quantum, atomistic, and continuum systems
Xuan Zhang, Limei Wang, ..., Yuanqi Du, ..., Shuiwang Ji
arXiv preprint arXiv:2307.08423 | paper

A Survey on Deep Graph Generation: Methods and Applications
Yuanqi Du*, Yanqiao Zhu*, Yinkai Wang*, Jieyu Zhang, Qiang Liu, Shu Wu
LoG 2022 | paper

Equivariant Graph Neural Networks with Complete Local Frames
Weitao Du*, He Zhang*, Yuanqi Du, Qi Meng, Wei Chen, Tie-Yan Liu, Nanning Zheng, Bin Shao
ICML 2022 | paper

Disentangled Spatiotemporal Graph Generative Models
Yuanqi Du*, Xiaojie Guo*, Hengning Cao, Yanfang Ye, Liang Zhao
AAAI 2022 (Oral) | paper

GraphGT: Machine Learning Datasets for Graph Generation and Transformation
Yuanqi Du*, Shiyu Wang*, Xiaojie Guo, ..., Liang Zhao
NeurIPS 2021 | paper

Deep Generative Model for Spatial Networks
Xiaojie Guo*, Yuanqi Du*, Liang Zhao.
KDD 2021 | paper