Frank Noé

Partner Research Manager

Publications

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- Enabling ab initio geometry optimization of strongly correlated systems with transferable deep quantum Monte Carlo
  
  P. Szab'o, Zeno Schatzle, Frank Noé
  
  March 2026
  
  Publication
- Scalable emulation of protein equilibrium ensembles with generative deep learning
  
  Sarah Lewis, Tim Hempel, Jose Jimenez-Luna, Michael Gastegger, Yu Xie, Andrew Y. K. Foong, Victor Garcia Satorras, Osama Abdin, Bastiaan S. Veeling, I. Zaporozhets, Yaoyi Chen, Soojung Yang, Arne Schneuing, Jigyasa Nigam, Federico Barbero, Vincent Stimper, Andrew Campbell, Jason Yim, Marten Lienen, Yu Shi, Shuxin Zheng, Hannes Schulz, Usman Munir, Cecilia Clementi, Frank Noé
  
  Science | July 2025
  
  Publication PDF Video Video Access Project
- An ab initio foundation model of wavefunctions that accurately describes chemical bond breaking
  
  Adam Foster, Zeno Schätzle, P. B. Szabó, Lixue Cheng, Jonas Köhler, Gino Cassella, Nicholas Gao, Jiawei Li, Frank Noé, Jan Hermann
  
  June 2025
  
  DOI Publication Publication Github
- Highly Accurate Real-space Electron Densities with Neural Networks
  
  Lixue Cheng, P. Bernát Szabó, Zeno Schätzle, Derk Kooi, Jonas Köhler, K.J.H. Giesbertz, Frank Noé, Jan Hermann, Paola Gori-Giorgi, Adam Foster
  
  The Journal of Chemical Physics | September 2024, Vol 162(3): pp. 034120
  
  DOI Preprint
- Navigating the Design Space of Equivariant Diffusion-Based Generative Models for De Novo 3D Molecule Generation
  
  Tuan Le, Julian Cremer, Frank Noé, Djork-Arné Clevert, Kristof Schütt
  
  ICLR 2024 | September 2023
  
  DOI PDF
- Rigid body flows for sampling molecular crystal structures
  
  Jonas Köhler, Michele Invernizzi, Pim de Haan, Frank Noé
  
  ICML 2023 | June 2023
  
  Publication
- Timewarp: Transferable Acceleration of Molecular Dynamics by Learning Time-Coarsened Dynamics
  
  Leon Klein, Andrew Y. K. Foong, Tor Erlend Fjelde, Bruno Mlodozeniec, Marc Brockschmidt, Sebastian Nowozin, Frank Noé, Ryota Tomioka
  
  NeurIPS 2023 | February 2023
  
  Publication
- Two for one: Diffusion models and force fields for coarse-grained molecular dynamics
  
  Marloes Arts, Victor Garcia Satorras, Chin-Wei Huang, Daniel Zügner, Marco Federici, Cecilia Clementi, Frank Noé, Rianne van den Berg, Robert Pinsler
  
  Journal of Chemical Theory and Computation | February 2023, Vol 19(18): pp. 6151-6159
  
  Publication Preprint
- Unsupervised Learning of Group Invariant and Equivariant Representations
  
  Robin Winter, Marco Bertolini, Tuan Le, Frank Noé, Djork-Arné Clevert
  
  2022 Neural Information Processing Systems | September 2022
  
  Publication
- Markov Field Models: scaling molecular kinetics approaches to large molecular machines
  
  Tim Hempel, Simon Olsson, Frank Noé
  
  September 2022
  
  Publication
- Ab-initio quantum chemistry with neural-network wavefunctions
  
  Jan Hermann, James Spencer, Kenny Choo, Antonio Mezzacapo, W. M. C. Foulkes, David Pfau, Giuseppe Carleo, Frank Noé
  
  August 2022
- Roadmap on Machine learning in electronic structure
  
  Frank Noé
  
  Electronic Structure | August 2022, Vol 4(2)
  
  Publication
- Flow-matching – efficient coarse-graining molecular dynamics without forces
  
  Jonas Köhler, Yaoyi Chen, Andreas Krämer, Cecilia Clementi, Frank Noé
  
  August 2022
  
  Publication
- Pareto-optimal cycles for power, efficiency and fluctuations of quantum heat engines using reinforcement learning
  
  Paolo Andrea Erdman, Alberto Rolandi, Paolo Abiuso, Martí Perarnau-Llobet, Frank Noé
  
  July 2022
  
  Publication
- Driving black-box quantum thermal machines with optimal power/efficiency trade-offs using reinforcement learning
  
  Paolo Andrea Erdman, Frank Noé
  
  April 2022
  
  Publication
- Deep learning to decompose macromolecules into independent Markovian domains
  
  Andreas Mardt, Tim Hempel, Cecilia Clementi, Frank Noé
  
  March 2022
  
  Publication
- Electronic excited states in deep variational Monte Carlo
  
  Mike Entwistle, Zeno Schätzle, Paolo A. Erdman, Jan Hermann, Frank Noé
  
  March 2022
  
  Publication
- Identifying optimal cycles in quantum thermal machines with reinforcement-learning
  
  Paolo A. Erdman, Frank Noé
  
  npj Quantum Information | January 2022, Vol 8
  
  Publication

- Enabling ab initio geometry optimization of strongly correlated systems with transferable deep quantum Monte Carlo
  
  P. Szab'o, Zeno Schatzle, Frank Noé
  
  March 2026
  
  Publication
- Enabling ab initio geometry optimization of strongly correlated systems with transferable deep quantum Monte Carlo
  
  P. Szab'o, Zeno Schatzle, Frank Noé
  
  March 2026
  
  Publication
- Scalable emulation of protein equilibrium ensembles with generative deep learning
  
  Sarah Lewis, Tim Hempel, Jose Jimenez-Luna, Michael Gastegger, Yu Xie, Andrew Y. K. Foong, Victor Garcia Satorras, Osama Abdin, Bastiaan S. Veeling, I. Zaporozhets, Yaoyi Chen, Soojung Yang, Arne Schneuing, Jigyasa Nigam, Federico Barbero, Vincent Stimper, Andrew Campbell, Jason Yim, Marten Lienen, Yu Shi, Shuxin Zheng, Hannes Schulz, Usman Munir, Cecilia Clementi, Frank Noé
  
  Science | July 2025
  
  Publication PDF Video Video Access Project
- An ab initio foundation model of wavefunctions that accurately describes chemical bond breaking
  
  Adam Foster, Zeno Schätzle, P. B. Szabó, Lixue Cheng, Jonas Köhler, Gino Cassella, Nicholas Gao, Jiawei Li, Frank Noé, Jan Hermann
  
  June 2025
  
  DOI Publication Publication Github
- Highly Accurate Real-space Electron Densities with Neural Networks
  
  Lixue Cheng, P. Bernát Szabó, Zeno Schätzle, Derk Kooi, Jonas Köhler, K.J.H. Giesbertz, Frank Noé, Jan Hermann, Paola Gori-Giorgi, Adam Foster
  
  The Journal of Chemical Physics | September 2024, Vol 162(3): pp. 034120
  
  DOI Preprint
- Navigating the Design Space of Equivariant Diffusion-Based Generative Models for De Novo 3D Molecule Generation
  
  Tuan Le, Julian Cremer, Frank Noé, Djork-Arné Clevert, Kristof Schütt
  
  ICLR 2024 | September 2023
  
  DOI PDF
- Rigid body flows for sampling molecular crystal structures
  
  Jonas Köhler, Michele Invernizzi, Pim de Haan, Frank Noé
  
  ICML 2023 | June 2023
  
  Publication
- Timewarp: Transferable Acceleration of Molecular Dynamics by Learning Time-Coarsened Dynamics
  
  Leon Klein, Andrew Y. K. Foong, Tor Erlend Fjelde, Bruno Mlodozeniec, Marc Brockschmidt, Sebastian Nowozin, Frank Noé, Ryota Tomioka
  
  NeurIPS 2023 | February 2023
  
  Publication
- Two for one: Diffusion models and force fields for coarse-grained molecular dynamics
  
  Marloes Arts, Victor Garcia Satorras, Chin-Wei Huang, Daniel Zügner, Marco Federici, Cecilia Clementi, Frank Noé, Rianne van den Berg, Robert Pinsler
  
  Journal of Chemical Theory and Computation | February 2023, Vol 19(18): pp. 6151-6159
  
  Publication Preprint
- Unsupervised Learning of Group Invariant and Equivariant Representations
  
  Robin Winter, Marco Bertolini, Tuan Le, Frank Noé, Djork-Arné Clevert
  
  2022 Neural Information Processing Systems | September 2022
  
  Publication
- Markov Field Models: scaling molecular kinetics approaches to large molecular machines
  
  Tim Hempel, Simon Olsson, Frank Noé
  
  September 2022
  
  Publication
- Ab-initio quantum chemistry with neural-network wavefunctions
  
  Jan Hermann, James Spencer, Kenny Choo, Antonio Mezzacapo, W. M. C. Foulkes, David Pfau, Giuseppe Carleo, Frank Noé
  
  August 2022
- Roadmap on Machine learning in electronic structure
  
  Frank Noé
  
  Electronic Structure | August 2022, Vol 4(2)
  
  Publication
- Flow-matching – efficient coarse-graining molecular dynamics without forces
  
  Jonas Köhler, Yaoyi Chen, Andreas Krämer, Cecilia Clementi, Frank Noé
  
  August 2022
  
  Publication
- Pareto-optimal cycles for power, efficiency and fluctuations of quantum heat engines using reinforcement learning
  
  Paolo Andrea Erdman, Alberto Rolandi, Paolo Abiuso, Martí Perarnau-Llobet, Frank Noé
  
  July 2022
  
  Publication
- Driving black-box quantum thermal machines with optimal power/efficiency trade-offs using reinforcement learning
  
  Paolo Andrea Erdman, Frank Noé
  
  April 2022
  
  Publication
- Deep learning to decompose macromolecules into independent Markovian domains
  
  Andreas Mardt, Tim Hempel, Cecilia Clementi, Frank Noé
  
  March 2022
  
  Publication
- Electronic excited states in deep variational Monte Carlo
  
  Mike Entwistle, Zeno Schätzle, Paolo A. Erdman, Jan Hermann, Frank Noé
  
  March 2022
  
  Publication
- Identifying optimal cycles in quantum thermal machines with reinforcement-learning
  
  Paolo A. Erdman, Frank Noé
  
  npj Quantum Information | January 2022, Vol 8
  
  Publication
- Enabling ab initio geometry optimization of strongly correlated systems with transferable deep quantum Monte Carlo
  
  P. Szab'o, Zeno Schatzle, Frank Noé
  
  March 2026
  
  Publication
- Pareto-optimal cycles for power, efficiency and fluctuations of quantum heat engines using reinforcement learning
  
  Paolo Andrea Erdman, Alberto Rolandi, Paolo Abiuso, Martí Perarnau-Llobet, Frank Noé
  
  July 2022
  
  Publication
- Driving black-box quantum thermal machines with optimal power/efficiency trade-offs using reinforcement learning
  
  Paolo Andrea Erdman, Frank Noé
  
  April 2022
  
  Publication
- Markov Field Models: scaling molecular kinetics approaches to large molecular machines
  
  Tim Hempel, Simon Olsson, Frank Noé
  
  September 2022
  
  Publication

- Enabling ab initio geometry optimization of strongly correlated systems with transferable deep quantum Monte Carlo
  
  P. Szab'o, Zeno Schatzle, Frank Noé
  
  March 2026
  
  Publication
- An ab initio foundation model of wavefunctions that accurately describes chemical bond breaking
  
  Adam Foster, Zeno Schätzle, P. B. Szabó, Lixue Cheng, Jonas Köhler, Gino Cassella, Nicholas Gao, Jiawei Li, Frank Noé, Jan Hermann
  
  June 2025
  
  DOI Publication Publication Github
- Markov Field Models: scaling molecular kinetics approaches to large molecular machines
  
  Tim Hempel, Simon Olsson, Frank Noé
  
  September 2022
  
  Publication
- Ab-initio quantum chemistry with neural-network wavefunctions
  
  Jan Hermann, James Spencer, Kenny Choo, Antonio Mezzacapo, W. M. C. Foulkes, David Pfau, Giuseppe Carleo, Frank Noé
  
  August 2022
- Flow-matching – efficient coarse-graining molecular dynamics without forces
  
  Jonas Köhler, Yaoyi Chen, Andreas Krämer, Cecilia Clementi, Frank Noé
  
  August 2022
  
  Publication
- Pareto-optimal cycles for power, efficiency and fluctuations of quantum heat engines using reinforcement learning
  
  Paolo Andrea Erdman, Alberto Rolandi, Paolo Abiuso, Martí Perarnau-Llobet, Frank Noé
  
  July 2022
  
  Publication
- Driving black-box quantum thermal machines with optimal power/efficiency trade-offs using reinforcement learning
  
  Paolo Andrea Erdman, Frank Noé
  
  April 2022
  
  Publication
- Deep learning to decompose macromolecules into independent Markovian domains
  
  Andreas Mardt, Tim Hempel, Cecilia Clementi, Frank Noé
  
  March 2022
  
  Publication
- Electronic excited states in deep variational Monte Carlo
  
  Mike Entwistle, Zeno Schätzle, Paolo A. Erdman, Jan Hermann, Frank Noé
  
  March 2022
  
  Publication
- Scalable emulation of protein equilibrium ensembles with generative deep learning
  
  Sarah Lewis, Tim Hempel, Jose Jimenez-Luna, Michael Gastegger, Yu Xie, Andrew Y. K. Foong, Victor Garcia Satorras, Osama Abdin, Bastiaan S. Veeling, I. Zaporozhets, Yaoyi Chen, Soojung Yang, Arne Schneuing, Jigyasa Nigam, Federico Barbero, Vincent Stimper, Andrew Campbell, Jason Yim, Marten Lienen, Yu Shi, Shuxin Zheng, Hannes Schulz, Usman Munir, Cecilia Clementi, Frank Noé
  
  Science | July 2025
  
  Publication PDF Video Video Access Project
- Highly Accurate Real-space Electron Densities with Neural Networks
  
  Lixue Cheng, P. Bernát Szabó, Zeno Schätzle, Derk Kooi, Jonas Köhler, K.J.H. Giesbertz, Frank Noé, Jan Hermann, Paola Gori-Giorgi, Adam Foster
  
  The Journal of Chemical Physics | September 2024, Vol 162(3): pp. 034120
  
  DOI Preprint
- Two for one: Diffusion models and force fields for coarse-grained molecular dynamics
  
  Marloes Arts, Victor Garcia Satorras, Chin-Wei Huang, Daniel Zügner, Marco Federici, Cecilia Clementi, Frank Noé, Rianne van den Berg, Robert Pinsler
  
  Journal of Chemical Theory and Computation | February 2023, Vol 19(18): pp. 6151-6159
  
  Publication Preprint
- Roadmap on Machine learning in electronic structure
  
  Frank Noé
  
  Electronic Structure | August 2022, Vol 4(2)
  
  Publication
- Identifying optimal cycles in quantum thermal machines with reinforcement-learning
  
  Paolo A. Erdman, Frank Noé
  
  npj Quantum Information | January 2022, Vol 8
  
  Publication
- Navigating the Design Space of Equivariant Diffusion-Based Generative Models for De Novo 3D Molecule Generation
  
  Tuan Le, Julian Cremer, Frank Noé, Djork-Arné Clevert, Kristof Schütt
  
  ICLR 2024 | September 2023
  
  DOI PDF
- Rigid body flows for sampling molecular crystal structures
  
  Jonas Köhler, Michele Invernizzi, Pim de Haan, Frank Noé
  
  ICML 2023 | June 2023
  
  Publication
- Timewarp: Transferable Acceleration of Molecular Dynamics by Learning Time-Coarsened Dynamics
  
  Leon Klein, Andrew Y. K. Foong, Tor Erlend Fjelde, Bruno Mlodozeniec, Marc Brockschmidt, Sebastian Nowozin, Frank Noé, Ryota Tomioka
  
  NeurIPS 2023 | February 2023
  
  Publication
- Unsupervised Learning of Group Invariant and Equivariant Representations
  
  Robin Winter, Marco Bertolini, Tuan Le, Frank Noé, Djork-Arné Clevert
  
  2022 Neural Information Processing Systems | September 2022
  
  Publication

Frank Noé

Publications

Enabling ab initio geometry optimization of strongly correlated systems with transferable deep quantum Monte Carlo

Scalable emulation of protein equilibrium ensembles with generative deep learning

An ab initio foundation model of wavefunctions that accurately describes chemical bond breaking

Highly Accurate Real-space Electron Densities with Neural Networks

Navigating the Design Space of Equivariant Diffusion-Based Generative Models for De Novo 3D Molecule Generation

Rigid body flows for sampling molecular crystal structures

Timewarp: Transferable Acceleration of Molecular Dynamics by Learning Time-Coarsened Dynamics

Two for one: Diffusion models and force fields for coarse-grained molecular dynamics

Unsupervised Learning of Group Invariant and Equivariant Representations

Markov Field Models: scaling molecular kinetics approaches to large molecular machines

Ab-initio quantum chemistry with neural-network wavefunctions

Roadmap on Machine learning in electronic structure

Flow-matching – efficient coarse-graining molecular dynamics without forces

Pareto-optimal cycles for power, efficiency and fluctuations of quantum heat engines using reinforcement learning

Driving black-box quantum thermal machines with optimal power/efficiency trade-offs using reinforcement learning

Deep learning to decompose macromolecules into independent Markovian domains

Electronic excited states in deep variational Monte Carlo

Identifying optimal cycles in quantum thermal machines with reinforcement-learning

Enabling ab initio geometry optimization of strongly correlated systems with transferable deep quantum Monte Carlo

Enabling ab initio geometry optimization of strongly correlated systems with transferable deep quantum Monte Carlo

Scalable emulation of protein equilibrium ensembles with generative deep learning

An ab initio foundation model of wavefunctions that accurately describes chemical bond breaking

Highly Accurate Real-space Electron Densities with Neural Networks

Navigating the Design Space of Equivariant Diffusion-Based Generative Models for De Novo 3D Molecule Generation

Rigid body flows for sampling molecular crystal structures

Timewarp: Transferable Acceleration of Molecular Dynamics by Learning Time-Coarsened Dynamics

Two for one: Diffusion models and force fields for coarse-grained molecular dynamics

Unsupervised Learning of Group Invariant and Equivariant Representations

Markov Field Models: scaling molecular kinetics approaches to large molecular machines

Ab-initio quantum chemistry with neural-network wavefunctions

Roadmap on Machine learning in electronic structure

Flow-matching – efficient coarse-graining molecular dynamics without forces

Pareto-optimal cycles for power, efficiency and fluctuations of quantum heat engines using reinforcement learning

Driving black-box quantum thermal machines with optimal power/efficiency trade-offs using reinforcement learning

Deep learning to decompose macromolecules into independent Markovian domains

Electronic excited states in deep variational Monte Carlo

Identifying optimal cycles in quantum thermal machines with reinforcement-learning

Enabling ab initio geometry optimization of strongly correlated systems with transferable deep quantum Monte Carlo

Pareto-optimal cycles for power, efficiency and fluctuations of quantum heat engines using reinforcement learning

Driving black-box quantum thermal machines with optimal power/efficiency trade-offs using reinforcement learning

Markov Field Models: scaling molecular kinetics approaches to large molecular machines

Enabling ab initio geometry optimization of strongly correlated systems with transferable deep quantum Monte Carlo

An ab initio foundation model of wavefunctions that accurately describes chemical bond breaking

Markov Field Models: scaling molecular kinetics approaches to large molecular machines

Ab-initio quantum chemistry with neural-network wavefunctions

Flow-matching – efficient coarse-graining molecular dynamics without forces

Pareto-optimal cycles for power, efficiency and fluctuations of quantum heat engines using reinforcement learning

Driving black-box quantum thermal machines with optimal power/efficiency trade-offs using reinforcement learning

Deep learning to decompose macromolecules into independent Markovian domains

Electronic excited states in deep variational Monte Carlo

Scalable emulation of protein equilibrium ensembles with generative deep learning

Highly Accurate Real-space Electron Densities with Neural Networks

Two for one: Diffusion models and force fields for coarse-grained molecular dynamics

Roadmap on Machine learning in electronic structure

Identifying optimal cycles in quantum thermal machines with reinforcement-learning

Navigating the Design Space of Equivariant Diffusion-Based Generative Models for De Novo 3D Molecule Generation

Rigid body flows for sampling molecular crystal structures

Timewarp: Transferable Acceleration of Molecular Dynamics by Learning Time-Coarsened Dynamics

Unsupervised Learning of Group Invariant and Equivariant Representations