Frank Noé

According to our database, Frank Noé authored at least 63 papers between 2006 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2024

Improved motif-scaffolding with SE(3) flow matching.

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

CoRR, 2024

2023

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

CoRR, 2023

Reaction coordinate flows for model reduction of molecular kinetics.

Hao Wu, Frank Noé

CoRR, 2023

Towards Predicting Equilibrium Distributions for Molecular Systems with Deep Learning.

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

CoRR, 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, Robert Pinsler, Rianne van den Berg

CoRR, 2023

Equivariant flow matching.

Leon Klein, Andreas Krämer, Frank Noé

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 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

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Rigid Body Flows for Sampling Molecular Crystal Structures.

Jonas Köhler, Michele Invernizzi, Pim de Haan, Frank Noé

Proceedings of the International Conference on Machine Learning, 2023

2022

Generating stable molecules using imitation and reinforcement learning.

Søren Ager Meldgaard, Jonas Köhler, Henrik Lund Mortensen, Mads-Peter V. Christiansen, Frank Noé, Bjørk Hammer

Mach. Learn. Sci. Technol., 2022

Deeptime: a Python library for machine learning dynamical models from time series data.

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

Mach. Learn. Sci. Technol., 2022

Simulation of ligand dissociation kinetics from the protein kinase PYK2.

Justin Spiriti, Frank Noé, Chung F. Wong

J. Comput. Chem., 2022

Machine Learning Coarse-Grained Potentials of Protein Thermodynamics.

Maciej Majewski, Adrià Pérez, Philipp Thölke, Stefan Doerr, Nicholas E. Charron, Toni Giorgino, Brooke E. Husic, Cecilia Clementi, Frank Noé, Gianni De Fabritiis

CoRR, 2022

Machine learning frontier orbital energies of nanodiamonds.

Thorren Kirschbaum, Börries von Seggern, Joachim Dzubiella, Annika Bande, Frank Noé

CoRR, 2022

Ab-initio quantum chemistry with neural-network wavefunctions.

Jan Hermann, James S. Spencer, Kenny Choo, Antonio Mezzacapo, W. Matthew C. Foulkes, David Pfau, Giuseppe Carleo, Frank Noé

CoRR, 2022

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

Paolo Andrea Erdman, Frank Noé

CoRR, 2022

Force-matching Coarse-Graining without Forces.

Jonas Köhler, Yaoyi Chen, Andreas Krämer, Cecilia Clementi, Frank Noé

CoRR, 2022

Equivariant Graph Attention Networks for Molecular Property Prediction.

Tuan Le, Frank Noé, Djork-Arné Clevert

CoRR, 2022

Unsupervised Learning of Group Invariant and Equivariant Representations.

Robin Winter, Marco Bertolini, Tuan Le, Frank Noé, Djork-Arné Clevert

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Representation Learning on Biomolecular Structures Using Equivariant Graph Attention.

Tuan Le, Frank Noé, Djork-Arné Clevert

Proceedings of the Learning on Graphs Conference, 2022

2021

Coupling Particle-Based Reaction-Diffusion Simulations with Reservoirs Mediated by Reaction-Diffusion PDEs.

Margarita Kostré, Christof Schütte, Frank Noé, Mauricio J. Del Razo

Multiscale Model. Simul., 2021

Symmetric and antisymmetric kernels for machine learning problems in quantum physics and chemistry.

Stefan Klus, Patrick Gelß, Feliks Nüske, Frank Noé

Mach. Learn. Sci. Technol., 2021

Identifying optimal cycles in quantum thermal machines with reinforcement-learning.

Paolo Andrea Erdman, Frank Noé

CoRR, 2021

Auto-Encoding Molecular Conformations.

Robin Winter, Frank Noé, Djork-Arné Clevert

CoRR, 2021

Permutation-Invariant Variational Autoencoder for Graph-Level Representation Learning.

Robin Winter, Frank Noé, Djork-Arné Clevert

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Smooth Normalizing Flows.

Jonas Köhler, Andreas Krämer, Frank Noé

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Parameterized Hypercomplex Graph Neural Networks for Graph Classification.

Tuan Le, Marco Bertolini, Frank Noé, Djork-Arné Clevert

Proceedings of the Artificial Neural Networks and Machine Learning - ICANN 2021, 2021

2020

Variational Approach for Learning Markov Processes from Time Series Data.

Hao Wu, Frank Noé

J. Nonlinear Sci., 2020

TorchMD: A deep learning framework for molecular simulations.

Stefan Doerr, Maciej Majewski, Adrià Pérez, Andreas Krämer, Cecilia Clementi, Frank Noé, Toni Giorgino, Gianni De Fabritiis

CoRR, 2020

Training Neural Networks with Property-Preserving Parameter Perturbations.

Andreas Krämer, Jonas Köhler, Frank Noé

CoRR, 2020

Convergence to the fixed-node limit in deep variational Monte Carlo.

Zeno Schätzle, Jan Hermann, Frank Noé

CoRR, 2020

Relevance of Rotationally Equivariant Convolutions for Predicting Molecular Properties.

Benjamin Kurt Miller, Mario Geiger, Tess E. Smidt, Frank Noé

CoRR, 2020

grünifai: interactive multiparameter optimization of molecules in a continuous vector space.

Robin Winter, Joren Retel, Frank Noé, Djork-Arné Clevert, Andreas Steffen

Bioinform., 2020

Stochastic Normalizing Flows.

Hao Wu, Jonas Köhler, Frank Noé

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Deep learning Markov and Koopman models with physical constraints.

Andreas Mardt, Luca Pasquali, Frank Noé, Hao Wu

Proceedings of Mathematical and Scientific Machine Learning, 2020

Equivariant Flows: Exact Likelihood Generative Learning for Symmetric Densities.

Jonas Köhler, Leon Klein, Frank Noé

Proceedings of the 37th International Conference on Machine Learning, 2020

2019

ReaDDy 2: Fast and flexible software framework for interacting-particle reaction dynamics.

Moritz Hoffmann, Christoph Fröhner, Frank Noé

PLoS Comput. Biol., 2019

Machine learning for molecular simulation.

Frank Noé, Alexandre Tkatchenko, Klaus-Robert Müller, Cecilia Clementi

CoRR, 2019

Generating valid Euclidean distance matrices.

Moritz Hoffmann, Frank Noé

CoRR, 2019

Equivariant Flows: sampling configurations for multi-body systems with symmetric energies.

Jonas Köhler, Leon Klein, Frank Noé

CoRR, 2019

Deep neural network solution of the electronic Schrödinger equation.

Jan Hermann, Zeno Schätzle, Frank Noé

CoRR, 2019

Porting Adaptive Ensemble Molecular Dynamics Workflows to the Summit Supercomputer.

John R. Ossyra, Ada Sedova, Arnold N. Tharrington, Frank Noé, Cecilia Clementi, Jeremy C. Smith

Proceedings of the High Performance Computing, 2019

2018

Data-Driven Model Reduction and Transfer Operator Approximation.

Stefan Klus, Feliks Nüske, Péter Koltai, Hao Wu, Ioannis G. Kevrekidis, Christof Schütte, Frank Noé

J. Nonlinear Sci., 2018

Machine Learning for Molecular Dynamics on Long Timescales.

Frank Noé

CoRR, 2018

Machine Learning of coarse-grained Molecular Dynamics Force Fields.

Jiang Wang, Christoph Wehmeyer, Frank Noé, Cecilia Clementi

CoRR, 2018

Boltzmann Generators - Sampling Equilibrium States of Many-Body Systems with Deep Learning.

Frank Noé, Hao Wu

CoRR, 2018

Optimal Data-Driven Estimation of Generalized Markov State Models for Non-Equilibrium Dynamics.

Péter Koltai, Hao Wu, Frank Noé, Christof Schütte

Comput., 2018

Deep Generative Markov State Models.

Hao Wu, Andreas Mardt, Luca Pasquali, Frank Noé

Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

2017

Time-lagged autoencoders: Deep learning of slow collective variables for molecular kinetics.

Christoph Wehmeyer, Frank Noé

CoRR, 2017

2016

Spectral Learning of Dynamic Systems from Nonequilibrium Data.

Hao Wu, Frank Noé

Proceedings of the Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, 2016

2015

Dynamical Organization of Syntaxin-1A at the Presynaptic Active Zone.

Alexander Ullrich, Mathias A. Böhme, Johannes Schöneberg, Harald Depner, Stephan J. Sigrist, Frank Noé

PLoS Comput. Biol., 2015

2014

Optimal Estimation of Free Energies and Stationary Densities from Multiple Biased Simulations.

Hao Wu, Frank Noé

Multiscale Model. Simul., 2014

2013

Spatiotemporal control of endocytosis by phosphatidylinositol-3, 4-bisphosphate.

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

Nat., 2013

A Variational Approach to Modeling Slow Processes in Stochastic Dynamical Systems.

Frank Noé, Feliks Nüske

Multiscale Model. Simul., 2013

2011

A flat Dirichlet process switching model for Bayesian estimation of hybrid systems.

Hao Wu, Frank Noé

Proceedings of the International Conference on Computational Science, 2011

Efficient Computation, Sensitivity, and Error Analysis of Committor Probabilities for Complex Dynamical Processes.

Jan-Hendrik Prinz, Martin Held, Jeremy C. Smith, Frank Noé

Multiscale Model. Simul., 2011

2010

Maximum a posteriori estimation for Markov chains based on Gaussian Markov random fields.

Hao Wu, Frank Noé

Proceedings of the International Conference on Computational Science, 2010

Probability Distance Based Compression of Hidden Markov Models.

Hao Wu, Frank Noé

Multiscale Model. Simul., 2010

On the Approximation Quality of Markov State Models.

Marco Sarich, Frank Noé, Christof Schütte

Multiscale Model. Simul., 2010

2007

A Comparative Study of Clustering Methods for Molecular Data.

Lin Wang, Minghu Jiang, Yinghua Lu, Minfu Sun, Frank Noé

Int. J. Neural Syst., 2007

Optimizing in Graphs with Expensive Computation of Edge Weights.

Frank Noé, Marcus Oswald, Gerhard Reinelt

Proceedings of the Operations Research, 2007

2006

Computing Best Transition Pathways in High-Dimensional Dynamical Systems: Application to the <i>Alpha_L</i> \leftrightharpoons <i>Beta</i> \leftrightharpoons <i>Alpha_R</i> Transitions in Octaalanine.

Frank Noé, Marcus Oswald, Gerhard Reinelt, Stefan Fischer, Jeremy C. Smith

Multiscale Model. Simul., 2006

Self-Organizing Map Clustering Analysis for Molecular Data.

Lin Wang, Minghu Jiang, Yinghua Lu, Frank Noé, Jeremy C. Smith

Proceedings of the Advances in Neural Networks - ISNN 2006, Third International Symposium on Neural Networks, Chengdu, China, May 28, 2006

Clustering Analysis of Competitive Learning Network for Molecular Data.

Lin Wang, Minghu Jiang, Yinghua Lu, Frank Noé, Jeremy C. Smith

Proceedings of the Advances in Neural Networks - ISNN 2006, Third International Symposium on Neural Networks, Chengdu, China, May 28, 2006