Frank Noé

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

December 2024

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

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

December 2024

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

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

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

December 2024

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

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

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

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