Frank Noé

Publication Activity (10 Years)

Years Active: 2006-2024
Publications (10 Years): 68

Top Topics

Molecular Dynamics

Machine Learning

Diffusion Models

Top Venues

Mach. Learn. Sci. Technol.

J. Nonlinear Sci.

Publications

Maximilian Schebek, Michele Invernizzi, Frank Noé, Jutta Rogal
Efficient mapping of phase diagrams with conditional normalizing flows. CoRR (2024)
Julian Cremer, Tuan Le, Frank Noé, Djork-Arné Clevert, Kristof T. Schütt
PILOT: Equivariant diffusion for pocket conditioned de novo ligand generation with multi-objective guidance via importance sampling. CoRR (2024)
Jason Yim, Andrew Campbell, Emile Mathieu, Andrew Y. K. Foong, Michael Gastegger, José Jiménez-Luna, Sarah Lewis, Victor Garcia Satorras, Bastiaan S. Veeling, Frank Noé, Regina Barzilay, Tommi S. Jaakkola
Improved motif-scaffolding with SE(3) flow matching. Trans. Mach. Learn. Res. 2024 (2024)
Jason Yim, Andrew Campbell, Emile Mathieu, Andrew Y. K. Foong, Michael Gastegger, José Jiménez-Luna, Sarah Lewis, Victor Garcia Satorras, Bastiaan S. Veeling, Frank Noé, Regina Barzilay, Tommi S. Jaakkola
Improved motif-scaffolding with SE(3) flow matching. CoRR (2024)
Tuan Le, Julian Cremer, Frank Noé, Djork-Arné Clevert, Kristof T. Schütt
Navigating the Design Space of Equivariant Diffusion-Based Generative Models for De Novo 3D Molecule Generation. ICLR (2024)
Leon Klein, Frank Noé
Transferable Boltzmann Generators. CoRR (2024)
Shuxin Zheng, Jiyan He, Chang Liu, Yu Shi, Ziheng Lu, Weitao Feng, Fusong Ju, Jiaxi Wang, Jianwei Zhu, Yaosen Min, He Zhang, Shidi Tang, Hongxia Hao, Peiran Jin, Chi Chen, Frank Noé, Haiguang Liu, Tie-Yan Liu
Predicting equilibrium distributions for molecular systems with deep learning. Nat. Mac. Intell. 6 (5) (2024)
Hao Wu, Frank Noé
Reaction coordinate flows for model reduction of molecular kinetics. CoRR (2023)
Tuan Le, Julian Cremer, Frank Noé, Djork-Arné Clevert, Kristof Schütt
Navigating the Design Space of Equivariant Diffusion-Based Generative Models for De Novo 3D Molecule Generation. CoRR (2023)
Jonas Köhler, Michele Invernizzi, Pim de Haan, Frank Noé
Rigid Body Flows for Sampling Molecular Crystal Structures. ICML (2023)
Leon Klein, Andrew Y. K. Foong, Tor Erlend Fjelde, Bruno Mlodozeniec, Marc Brockschmidt, Sebastian Nowozin, Frank Noé, Ryota Tomioka
Timewarp: Transferable Acceleration of Molecular Dynamics by Learning Time-Coarsened Dynamics. CoRR (2023)
Leon Klein, Andreas Krämer, Frank Noé
Equivariant flow matching. NeurIPS (2023)
Leon Klein, Andrew Y. K. Foong, Tor Erlend Fjelde, Bruno Mlodozeniec, Marc Brockschmidt, Sebastian Nowozin, Frank Noé, Ryota Tomioka
Timewarp: Transferable Acceleration of Molecular Dynamics by Learning Time-Coarsened Dynamics. NeurIPS (2023)
Marloes Arts, Victor Garcia Satorras, Chin-Wei Huang, Daniel Zügner, Marco Federici, Cecilia Clementi, Frank Noé, Robert Pinsler, Rianne van den Berg
Two for One: Diffusion Models and Force Fields for Coarse-Grained Molecular Dynamics. CoRR (2023)
Jonas Köhler, Michele Invernizzi, Pim de Haan, Frank Noé
Rigid body flows for sampling molecular crystal structures. CoRR (2023)
Shuxin Zheng, Jiyan He, Chang Liu, Yu Shi, Ziheng Lu, Weitao Feng, Fusong Ju, Jiaxi Wang, Jianwei Zhu, Yaosen Min, He Zhang, Shidi Tang, Hongxia Hao, Peiran Jin, Chi Chen, Frank Noé, Haiguang Liu, Tie-Yan Liu
Towards Predicting Equilibrium Distributions for Molecular Systems with Deep Learning. CoRR (2023)
Leon Klein, Andreas Krämer, Frank Noé
Equivariant flow matching. CoRR (2023)
Jonas Köhler, Yaoyi Chen, Andreas Krämer, Cecilia Clementi, Frank Noé
Force-matching Coarse-Graining without Forces. CoRR (2022)
Moritz Hoffmann, Martin Scherer, Tim Hempel, Andreas Mardt, Brian de Silva, Brooke E. Husic, Stefan Klus, Hao Wu, J. Nathan Kutz, Steven L. Brunton, Frank Noé
Deeptime: a Python library for machine learning dynamical models from time series data. Mach. Learn. Sci. Technol. 3 (1) (2022)
Tuan Le, Frank Noé, Djork-Arné Clevert
Equivariant Graph Attention Networks for Molecular Property Prediction. CoRR (2022)
Robin Winter, Marco Bertolini, Tuan Le, Frank Noé, Djork-Arné Clevert
Unsupervised Learning of Group Invariant and Equivariant Representations. CoRR (2022)
Paolo Andrea Erdman, Frank Noé
Driving black-box quantum thermal machines with optimal power/efficiency trade-offs using reinforcement learning. CoRR (2022)
Justin Spiriti, Frank Noé, Chung F. Wong
Simulation of ligand dissociation kinetics from the protein kinase PYK2. J. Comput. Chem. 43 (28) (2022)
Jan Hermann, James Spencer, Kenny Choo, Antonio Mezzacapo, W. Matthew C. Foulkes, David Pfau, Giuseppe Carleo, Frank Noé
Ab-initio quantum chemistry with neural-network wavefunctions. CoRR (2022)
Robin Winter, Marco Bertolini, Tuan Le, Frank Noé, Djork-Arné Clevert
Unsupervised Learning of Group Invariant and Equivariant Representations. NeurIPS (2022)
Thorren Kirschbaum, Börries von Seggern, Joachim Dzubiella, Annika Bande, Frank Noé
Machine learning frontier orbital energies of nanodiamonds. CoRR (2022)
Søren Ager Meldgaard, Jonas Köhler, Henrik Lund Mortensen, Mads-Peter V. Christiansen, Frank Noé, Bjørk Hammer
Generating stable molecules using imitation and reinforcement learning. Mach. Learn. Sci. Technol. 3 (1) (2022)
Tuan Le, Frank Noé, Djork-Arné Clevert
Representation Learning on Biomolecular Structures Using Equivariant Graph Attention. LoG (2022)
Maciej Majewski, Adrià Pérez, Philipp Thölke, Stefan Doerr, Nicholas E. Charron, Toni Giorgino, Brooke E. Husic, Cecilia Clementi, Frank Noé, Gianni De Fabritiis
Machine Learning Coarse-Grained Potentials of Protein Thermodynamics. CoRR (2022)
Robin Winter, Frank Noé, Djork-Arné Clevert
Auto-Encoding Molecular Conformations. CoRR (2021)
Jonas Köhler, Andreas Krämer, Frank Noé
Smooth Normalizing Flows. CoRR (2021)
Margarita Kostré, Christof Schütte, Frank Noé, Mauricio J. Del Razo
Coupling Particle-Based Reaction-Diffusion Simulations with Reservoirs Mediated by Reaction-Diffusion PDEs. Multiscale Model. Simul. 19 (4) (2021)
Robin Winter, Frank Noé, Djork-Arné Clevert
Permutation-Invariant Variational Autoencoder for Graph-Level Representation Learning. NeurIPS (2021)
Stefan Klus, Patrick Gelß, Feliks Nüske, Frank Noé
Symmetric and antisymmetric kernels for machine learning problems in quantum physics and chemistry. Mach. Learn. Sci. Technol. 2 (4) (2021)
Paolo Andrea Erdman, Frank Noé
Identifying optimal cycles in quantum thermal machines with reinforcement-learning. CoRR (2021)
Jonas Köhler, Andreas Krämer, Frank Noé
Smooth Normalizing Flows. NeurIPS (2021)
Tuan Le, Marco Bertolini, Frank Noé, Djork-Arné Clevert
Parameterized Hypercomplex Graph Neural Networks for Graph Classification. ICANN (3) (2021)
Tuan Le, Marco Bertolini, Frank Noé, Djork-Arné Clevert
Parameterized Hypercomplex Graph Neural Networks for Graph Classification. CoRR (2021)
Moritz Hoffmann, Martin Scherer, Tim Hempel, Andreas Mardt, Brian de Silva, Brooke E. Husic, Stefan Klus, Hao Wu, J. Nathan Kutz, Steven L. Brunton, Frank Noé
Deeptime: a Python library for machine learning dynamical models from time series data. CoRR (2021)
Søren Ager Meldgaard, Jonas Köhler, Henrik Lund Mortensen, Mads-Peter V. Christiansen, Frank Noé, Bjørk Hammer
Generating stable molecules using imitation and reinforcement learning. CoRR (2021)
Robin Winter, Frank Noé, Djork-Arné Clevert
Permutation-Invariant Variational Autoencoder for Graph-Level Representation Learning. CoRR (2021)
Stefan Doerr, Maciej Majewski, Adrià Pérez, Andreas Krämer, Cecilia Clementi, Frank Noé, Toni Giorgino, Gianni De Fabritiis
TorchMD: A deep learning framework for molecular simulations. CoRR (2020)
Jonas Köhler, Leon Klein, Frank Noé
Equivariant Flows: exact likelihood generative learning for symmetric densities. CoRR (2020)
Robin Winter, Joren Retel, Frank Noé, Djork-Arné Clevert, Andreas Steffen
grünifai: interactive multiparameter optimization of molecules in a continuous vector space. Bioinform. 36 (13) (2020)
Andreas Mardt, Luca Pasquali, Frank Noé, Hao Wu
Deep learning Markov and Koopman models with physical constraints. MSML (2020)
Hao Wu, Jonas Köhler, Frank Noé
Stochastic Normalizing Flows. CoRR (2020)
Hao Wu, Frank Noé
Variational Approach for Learning Markov Processes from Time Series Data. J. Nonlinear Sci. 30 (1) (2020)
Andreas Krämer, Jonas Köhler, Frank Noé
Training Neural Networks with Property-Preserving Parameter Perturbations. CoRR (2020)
Hao Wu, Jonas Köhler, Frank Noé
Stochastic Normalizing Flows. NeurIPS (2020)
Jonas Köhler, Leon Klein, Frank Noé
Equivariant Flows: Exact Likelihood Generative Learning for Symmetric Densities. ICML (2020)
Benjamin Kurt Miller, Mario Geiger, Tess E. Smidt, Frank Noé
Relevance of Rotationally Equivariant Convolutions for Predicting Molecular Properties. CoRR (2020)
Zeno Schätzle, Jan Hermann, Frank Noé
Convergence to the fixed-node limit in deep variational Monte Carlo. CoRR (2020)
Jan Hermann, Zeno Schätzle, Frank Noé
Deep neural network solution of the electronic Schrödinger equation. CoRR (2019)
John R. Ossyra, Ada Sedova, Arnold N. Tharrington, Frank Noé, Cecilia Clementi, Jeremy C. Smith
Porting Adaptive Ensemble Molecular Dynamics Workflows to the Summit Supercomputer. ISC Workshops (2019)
Moritz Hoffmann, Frank Noé
Generating valid Euclidean distance matrices. CoRR (2019)
Moritz Hoffmann, Christoph Fröhner, Frank Noé
ReaDDy 2: Fast and flexible software framework for interacting-particle reaction dynamics. PLoS Comput. Biol. 15 (2) (2019)
Frank Noé, Alexandre Tkatchenko, Klaus-Robert Müller, Cecilia Clementi
Machine learning for molecular simulation. CoRR (2019)
Jonas Köhler, Leon Klein, Frank Noé
Equivariant Flows: sampling configurations for multi-body systems with symmetric energies. CoRR (2019)
Frank Noé
Machine Learning for Molecular Dynamics on Long Timescales. CoRR (2018)
Frank Noé, Hao Wu
Boltzmann Generators - Sampling Equilibrium States of Many-Body Systems with Deep Learning. CoRR (2018)
Hao Wu, Andreas Mardt, Luca Pasquali, Frank Noé
Deep Generative Markov State Models. CoRR (2018)
Jiang Wang, Christoph Wehmeyer, Frank Noé, Cecilia Clementi
Machine Learning of coarse-grained Molecular Dynamics Force Fields. CoRR (2018)
Hao Wu, Andreas Mardt, Luca Pasquali, Frank Noé
Deep Generative Markov State Models. NeurIPS (2018)
Péter Koltai, Hao Wu, Frank Noé, Christof Schütte
Optimal Data-Driven Estimation of Generalized Markov State Models for Non-Equilibrium Dynamics. Comput. 6 (1) (2018)
Stefan Klus, Feliks Nüske, Péter Koltai, Hao Wu, Ioannis G. Kevrekidis, Christof Schütte, Frank Noé
Data-Driven Model Reduction and Transfer Operator Approximation. J. Nonlinear Sci. 28 (3) (2018)
Christoph Wehmeyer, Frank Noé
Time-lagged autoencoders: Deep learning of slow collective variables for molecular kinetics. CoRR (2017)
Hao Wu, Frank Noé
Spectral Learning of Dynamic Systems from Nonequilibrium Data. NIPS (2016)
Hao Wu, Frank Noé
Spectral learning of dynamic systems from nonequilibrium data. CoRR (2016)
Alexander Ullrich, Mathias A. Böhme, Johannes Schöneberg, Harald Depner, Stephan J. Sigrist, Frank Noé
Dynamical Organization of Syntaxin-1A at the Presynaptic Active Zone. PLoS Comput. Biol. 11 (9) (2015)
Hao Wu, Frank Noé
Optimal Estimation of Free Energies and Stationary Densities from Multiple Biased Simulations. Multiscale Model. Simul. 12 (1) (2014)
York Posor, Marielle Eichhorn-Gruenig, Dmytro Puchkov, Johannes Schöneberg, Alexander Ullrich, André Lampe, Rainer Müller, Sirus Zarbakhsh, Federico Gulluni, Emilio Hirsch, Michael Krauss, Carsten Schultz, Jan Schmoranzer, Frank Noé, Volker Haucke
Spatiotemporal control of endocytosis by phosphatidylinositol-3, 4-bisphosphate. Nat. 499 (7457) (2013)
Frank Noé, Feliks Nüske
A Variational Approach to Modeling Slow Processes in Stochastic Dynamical Systems. Multiscale Model. Simul. 11 (2) (2013)
Jan-Hendrik Prinz, Martin Held, Jeremy C. Smith, Frank Noé
Efficient Computation, Sensitivity, and Error Analysis of Committor Probabilities for Complex Dynamical Processes. Multiscale Model. Simul. 9 (2) (2011)
Hao Wu, Frank Noé
A flat Dirichlet process switching model for Bayesian estimation of hybrid systems. ICCS (2011)
Hao Wu, Frank Noé
Maximum a posteriori estimation for Markov chains based on Gaussian Markov random fields. ICCS (1) (2010)
Marco Sarich, Frank Noé, Christof Schütte
On the Approximation Quality of Markov State Models. Multiscale Model. Simul. 8 (4) (2010)
Hao Wu, Frank Noé
Probability Distance Based Compression of Hidden Markov Models. Multiscale Model. Simul. 8 (5) (2010)
Frank Noé, Marcus Oswald, Gerhard Reinelt
Optimizing in Graphs with Expensive Computation of Edge Weights. OR (2007)
Lin Wang, Minghu Jiang, Yinghua Lu, Minfu Sun, Frank Noé
A Comparative Study of Clustering Methods for Molecular Data. Int. J. Neural Syst. 17 (6) (2007)
Frank Noé, Marcus Oswald, Gerhard Reinelt, Stefan Fischer, Jeremy C. Smith
Transitions in Octaalanine. Multiscale Model. Simul. 5 (2) (2006)
Lin Wang, Minghu Jiang, Yinghua Lu, Frank Noé, Jeremy C. Smith
Clustering Analysis of Competitive Learning Network for Molecular Data. ISNN (1) (2006)
Lin Wang, Minghu Jiang, Yinghua Lu, Frank Noé, Jeremy C. Smith
Self-Organizing Map Clustering Analysis for Molecular Data. ISNN (1) (2006)