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
Frank Noé
Partner Research Manager
Publications
Publications by Year
-
Rigid body flows for sampling molecular crystal structures
Jonas Köhler, Michele Invernizzi, Pim de Haan, Frank Noé
ICML 2023 | June 2023
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
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
-
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
Markov Field Models: scaling molecular kinetics approaches to large molecular machines
Tim Hempel, Simon Olsson, Frank Noé
September 2022
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
Electronic Structure | August 2022, Vol 4(2)
Flow-matching – efficient coarse-graining molecular dynamics without forces
Jonas Köhler, Yaoyi Chen, Andreas Krämer, Cecilia Clementi, Frank Noé
August 2022
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
Driving black-box quantum thermal machines with optimal power/efficiency trade-offs using reinforcement learning
Paolo Andrea Erdman, Frank Noé
April 2022
Deep learning to decompose macromolecules into independent Markovian domains
Andreas Mardt, Tim Hempel, Cecilia Clementi, Frank Noé
March 2022
Electronic excited states in deep variational Monte Carlo
Mike Entwistle, Zeno Schätzle, Paolo A. Erdman, Jan Hermann, Frank Noé
March 2022
Identifying optimal cycles in quantum thermal machines with reinforcement-learning
Paolo A. Erdman, Frank Noé
npj Quantum Information | January 2022, Vol 8
Publications by Research Area
-
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
Rigid body flows for sampling molecular crystal structures
Jonas Köhler, Michele Invernizzi, Pim de Haan, Frank Noé
ICML 2023 | June 2023
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
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
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
Markov Field Models: scaling molecular kinetics approaches to large molecular machines
Tim Hempel, Simon Olsson, Frank Noé
September 2022
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
Electronic Structure | August 2022, Vol 4(2)
Flow-matching – efficient coarse-graining molecular dynamics without forces
Jonas Köhler, Yaoyi Chen, Andreas Krämer, Cecilia Clementi, Frank Noé
August 2022
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
Driving black-box quantum thermal machines with optimal power/efficiency trade-offs using reinforcement learning
Paolo Andrea Erdman, Frank Noé
April 2022
Deep learning to decompose macromolecules into independent Markovian domains
Andreas Mardt, Tim Hempel, Cecilia Clementi, Frank Noé
March 2022
Electronic excited states in deep variational Monte Carlo
Mike Entwistle, Zeno Schätzle, Paolo A. Erdman, Jan Hermann, Frank Noé
March 2022
Identifying optimal cycles in quantum thermal machines with reinforcement-learning
Paolo A. Erdman, Frank Noé
npj Quantum Information | January 2022, Vol 8
-
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
Driving black-box quantum thermal machines with optimal power/efficiency trade-offs using reinforcement learning
Paolo Andrea Erdman, Frank Noé
April 2022
-
Markov Field Models: scaling molecular kinetics approaches to large molecular machines
Tim Hempel, Simon Olsson, Frank Noé
September 2022
Publications by Type
-
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
Rigid body flows for sampling molecular crystal structures
Jonas Köhler, Michele Invernizzi, Pim de Haan, Frank Noé
ICML 2023 | June 2023
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
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
-
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
Roadmap on Machine learning in electronic structure
Electronic Structure | August 2022, Vol 4(2)
Identifying optimal cycles in quantum thermal machines with reinforcement-learning
Paolo A. Erdman, Frank Noé
npj Quantum Information | January 2022, Vol 8
-
Markov Field Models: scaling molecular kinetics approaches to large molecular machines
Tim Hempel, Simon Olsson, Frank Noé
September 2022
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
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
Driving black-box quantum thermal machines with optimal power/efficiency trade-offs using reinforcement learning
Paolo Andrea Erdman, Frank Noé
April 2022
Deep learning to decompose macromolecules into independent Markovian domains
Andreas Mardt, Tim Hempel, Cecilia Clementi, Frank Noé
March 2022
Electronic excited states in deep variational Monte Carlo
Mike Entwistle, Zeno Schätzle, Paolo A. Erdman, Jan Hermann, Frank Noé
March 2022