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You can view all the papers in reverse
chronological order, sets of papers related to broad categories such as
machine learning,
natural language processing,
biology,
chemistry,
or physics, or papers in more specific areas including
game theory, inference, semi-supervised learning , information retrieval, or
reinforcement learning.
The list does not include all recent preprints from arXiv or
bioRxiv.
For a more complete list, see my
Google scholar page
Physics papers
- Bowen Jing, Bonnie Berger, and Tommi Jaakkola.
AI-based methods for simulating, sampling, and predicting protein ensembles.
Current Opinion in Structural Biology, 98. 2026.
[link]
- Ryotaro Okabe, Mouyang Cheng, Abhijatmedhi Chotrattanapituk, Manasi Mandal, Kiran Mak, Denisse Cordova Carrizales, Nguyen Tuan Hung, Xiang Fu, Bowen Han, Yao Wang, Weiwei Xie, Robert J. Cava, Tommi S. Jaakkola, Yongqiang Cheng, and Mingda Li.
Structural constraint integration in a generative model for the discovery of quantum materials.
Nature Materials, 2025.
[link]
- Jeet Mohapatra, Nima Dehmamy, Csaba Both, Subhro Das, and Tommi Jaakkola.
Symmetry-Driven Discovery of Dynamical Variables in Molecular Simulations.
International Conference on Machine Learning (ICML), 2025.
[link]
- Peter Holderrieth, Yilun Xu, and Tommi Jaakkola.
Hamiltonian Score Matching and Generative Flows.
Neural Information Processing Systems (NeurIPS), 2024.
[link]
- Xiang Fu, Andrew Rosen, Kyle Bystrom, Rui Wang, Albert Musaelian, Boris Kozinsky, Tess Smidt, and Tommi Jaakkola.
A Recipe for Charge Density Prediction.
Neural Information Processing Systems (NeurIPS), 2024.
[link]
- Nima Dehmamy, Csaba Both, Jeet Mohapatra, Subhro Das, and Tommi Jaakkola.
Neural Network Reparametrization for Accelerated Optimization in Molecular Simulations.
Neural Information Processing Systems (NeurIPS), 2024.
[link]
- Bowen Jing, Hannes Stark, Tommi Jaakkola, and Bonnie Berger.
Generative Modeling of Molecular Dynamics Trajectories.
Neural Information Processing Systems (NeurIPS), 2024.
[link]
- Ryotaro Okabe, Abhijatmedhi Chotrattanapituk, Artittaya Boonkird, Nina Andrejevic, Xiang Fu, Tommi S. Jaakkola, Qichen Song, Thanh Nguyen, Nathan Drucker, Sai Mu, Yao Wang, Bolin Liao, Yongqiang Cheng, and Mingda Li.
Virtual node graph neural network for full phonon prediction.
Nature Computational Science, 4(7). 2024.
[link]
- Xiang Fu, Tian Xie, Andrew S. Rosen, Tommi Jaakkola, and Jake Smith.
MOFDiff: Coarse-grained Diffusion for Metal-Organic Framework Design.
The 12th International Conference on Learning Representations (ICLR), 2024.
[link]
- Xiang Fu, Tian Xie, Nathan J. Rebello, Bradley D. Olsen, and Tommi Jaakkola.
Simulate Time-integrated Coarse-grained Molecular Dynamics with Multi-scale Graph Networks.
Transactions on Machine Learning Research (TMLR), 2023.
[link]
- Xiang Fu, Zhenghao Wu, Wujie Wang, Tian Xie, Sinan Keten, Rafael Gomez-Bombarelli, and Tommi Jaakkola.
Forces are not Enough: Benchmark and Critical Evaluation for Machine Learning Force Fields with Molecular Simulations.
Transactions on Machine Learning Research (TMLR), 2023.
[link]
- Yilun Xu, Ziming Liu, Max Tegmark, and Tommi Jaakkola.
Poisson Flow Generative Models.
Neural Information Processing Systems (NeurIPS), 2022.
[link]
- A. Friberg, T. Jaakkola, and J. Tuovinen.
Electromagnetic gaussian beam beyond the paraxial regime.
IEEE Transactions of Antennas and Propagation, 1992.
- A. Vasara, M. Taghizadeh, J. Turunen, Westerholmand E. Noponen J., H. Ichikawa, J. Miller, T. Jaakkola, and S. Kuisma.
Binary surface-relief gratings for array illumination in digital optics.
Applied Optics, 31(17), pp. 3320-3336. 1992.
