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

This page is updated periodically, mainly for selected work. For an exhaustive list, please check my Google Scholar.

JANUS: A Multi-modal Foundation Neural Sampler for Disordered Materials
Denis Blessing, Mouyang Cheng, Maximilian Schebek, Jutta Rogal, Mingda Li, Carles Domingo-Enrich†, Yuanqi Du†
arXiv preprint arXiv:2608.19116 | paper

ATLAS: A Foundation Neural Sampler for Amorphous Materials
Mouyang Cheng, Denis Blessing, Botao Yu, Gerhard Neumann, Mingda Li, Carles Domingo-Enrich†, Yuanqi Du†
arXiv preprint arXiv:2607.19198 | paper

Rare Event Analysis via Stochastic Optimal Control
Yuanqi Du, Jiajun He, Dinghuai Zhang, Eric Vanden-Eijnden, Carles Domingo-Enrich
arXiv preprint arXiv:2604.13213 | paper

Free energy Estimation on Any State Space
Jiajun He, Zijing Ou, Francisco Vargas, ..., Yuanqi Du†
arXiv preprint arXiv:2605.31063 | paper

RNE: A Plug-and-play Framework for Diffusion Density Estimation and Inference-time Control
Jiajun He, José Miguel Hernández-Lobato, Yuanqi Du†, Francisco Vargas†
ICLR 2026 | paper

Measuring AI Scientists: From Exams to Discovery
Yuanqi Du, Steven Dillmann, Jon Laurent, Peter Jansen, Haojun Jia, Ludwig Schmidt, Andrew White, Kristin Persson, Chenru Duan
ChemRxiv preprint | paper

DrugSAGE: Self-evolving Agent Experience for Efficient State-of-the-Art Drug Discovery
Yikun Zhang, Xiwei Cheng, Tianyu Liu, Yuanqi Du, Wengong Jin
arXiv preprint arXiv:2605.15461 | paper

A Priori Sampling of Transition States with Guided Diffusion
Hyukjun Lim, Soojung Yang, Lucas Pinède, Miguel Steiner, Yuanqi Du, Rafael Gómez-Bombarelli
arXiv preprint arXiv:2603.25980 | paper

Assessing Generative Modeling Approaches for Free Energy Estimates in Condensed Matter
Maximilian Schebek, Jiajun He, Emil Hoffmann, Yuanqi Du, Frank Noé, Jutta Rogal
The Journal of Chemical Physics 2026 | paper

The Rise of Generative AI for Metal-Organic Framework Design and Synthesis
Chenru Duan, Aditya Nandy, Shyam Chand Pal, Xin Yang, Wenhao Gao, Yuanqi Du, et al.
Matter 2026 | paper

Towards Diverse Scientific Hypothesis Search with Large Language Models
Haorui Wang, Parshin Shojaee, Kazem Meidani, ... Jiajun He†, Chandan K. Reddy†, Chao Zhang†, Yuanqi Du†
ICML 2026 | paper

CREPE: Controlling Diffusion with Replica Exchange
Jiajun He, ..., Yuanqi Du, Saifuddin Syed, Francisco Vargas
ICLR 2026 | paper

Accelerated Parallel Tempering via Neural Transports
Leo Zhang, Peter Potaptchik, Jiajun He, Yuanqi Du, ..., Saifuddin Syed
ICLR 2026 | paper

SAGA: Accelerating Scientific Discovery with Autonomous Goal-evolving Agents
Yuanqi Du*, Botao Yu*, Tianyu Liu*, et al
arXiv preprint arXiv:2512.21782 | paper

FEAT: Free energy Estimators with Adaptive Transport
Yuanqi Du*, Jiajun He*, Francisco Vargas, ..., Eric Vanden-Eijnden
NeurIPS 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 (Cover Article) | 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

Building-Block Aware Generative Modeling for 3D Crystals of Metal Organic Frameworks
Chenru Duan, Aditya Nandy, Sizhan Liu, Yuanqi Du, Liu He, Yi Qu, Haojun Jia, Jin-Hu Dou
arXiv preprint arXiv:2505.08531 | paper

Evaluating Large Language Models in Scientific Discovery
Zhangde Song*, Jieyu Lu*, Yuanqi Du*, et al
arXiv preprint arXiv:2512.15567 | paper

Trust Region Constrained Measure Transport in Path Space for Stochastic Optimal Control and Inference
Denis Blessing, Julius Berner, Lorenz Richter, Carles Domingo-Enrich, Yuanqi Du, Arash Vahdat, Gerhard Neumann
NeurIPS 2025 (Spotlight) | paper

LLM-Augmented Chemical Synthesis and Design Decision Programs
Haorui Wang, Jeff Guo, Lingkai Kong, Rampi Ramprasad, Philippe Schwaller, Yuanqi Du†, Chao Zhang†
ICML 2025 | paper

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
ICLR 2025 FPI Workshop | 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

Generative Design of Functional Metal Complexes Utilizing the Internal Knowledge and Reasoning Capability of Large Language Models
Jieyu Lu, Zhangde Song, Qiyuan Zhao, Yuanqi Du, Yirui Cao, Haojun Jia, Chenru Duan
JACS 2025 (Cover Article) | 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†
npj Computational Materials 2025 | paper

Artificial Intelligence for Science in Quantum, Atomistic, and Continuum Systems
Xuan Zhang, Limei Wang, ..., Yuanqi Du, ..., Shuiwang Ji
Foundations and Trends® in Machine Learning 2025 | paper

Graph Generative Pre-trained Transformer
Xiaohui Chen, Yinkai Wang, Jiaxing He, Yuanqi Du, Soha Hassoun, Xiaolin Xu, Li-Ping Liu
ICML 2025 | 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

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

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

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

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

MUBen: Benchmarking the Uncertainty of Molecular Representation Models
Yinghao Li, Lingkai Kong, Yuanqi Du, Yue Yu, Yuchen Zhuang, Wenhao Mu, Chao Zhang
TMLR 2024 | 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 Article) | 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

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

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