Frank Noé - RankMe

101 papers A* 12Misc 2Journal 79Unranked 8

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	Boltzmann Generators for Condensed Matter via Riemannian Flow Matching. CoRR Emil Hoffmann, Maximilian Schebek, Leon Klein, Frank Noé, Jutta Rogal
2026	J	`jnl`	Enabling ab initio geometry optimization of strongly correlated systems with transferable deep quantum Monte Carlo. CoRR P. Bernát Szabó, Zeno Schätzle, Frank Noé
2026	J	`jnl`	Enhanced Diffusion Sampling: Efficient Rare Event Sampling and Free Energy Calculation with Diffusion Models. CoRR Yu Xie, Ludwig Winkler, Lixin Sun, Sarah Lewis, Adam Evan Foster, José Jiménez-Luna, Tim Hempel, Michael Gastegger, Yaoyi Chen, Iryna Zaporozhets, Cecilia Clementi, Christopher M. Bishop, Frank Noé
2026	J	`jnl`	Excited Pfaffians: Generalized Neural Wave Functions Across Structure and State. CoRR Nicholas Gao, Till Grutschus, Frank Noé, Stephan Günnemann
2026	J	`jnl`	Learning Hamiltonian Flow Maps: Mean Flow Consistency for Large-Timestep Molecular Dynamics. CoRR Winfried Ripken, Michael Plainer, Gregor Lied, J. Thorben Frank, Oliver T. Unke, Stefan Chmiela, Frank Noé, Klaus-Robert Müller
2025	J	`jnl`	Ab-initio simulation of excited-state potential energy surfaces with transferable deep quantum Monte Carlo. CoRR Zeno Schätzle, P. Bernát Szabó, Alice Cuzzocrea, Frank Noé
2025	J	`jnl`	Accelerating scientific discovery with the common task framework. CoRR J. Nathan Kutz, Peter W. Battaglia, Michael P. Brenner, Kevin Carlberg, Aric A. Hagberg, Shirley Ho, Stephan Hoyer, Henning Lange, Hod Lipson, Michael W. Mahoney, Frank Noé, Max Welling, Laure Zanna, Francis Zhu, Steven L. Brunton
2025	J	`jnl`	An ab initio foundation model of wavefunctions that accurately describes chemical bond breaking. CoRR Adam Foster, Zeno Schätzle, P. Bernát Szabó, Lixue Cheng, Jonas Köhler, Gino Cassella, Nicholas Gao, Jiawei Li, Frank Noé, Jan Hermann
2025	J	`jnl`	Assessing generative modeling approaches for free energy estimates in condensed matter. CoRR Maximilian Schebek, Jiajun He, Emil Hoffmann, Yuanqi Du, Frank Noé, Jutta Rogal
2025	J	`jnl`	Consistent Sampling and Simulation: Molecular Dynamics with Energy-Based Diffusion Models. CoRR Michael Plainer, Hao Wu, Leon Klein, Stephan Günnemann, Frank Noé
2025	J	`jnl`	Operator Forces For Coarse-Grained Molecular Dynamics. CoRR Leon Klein, Atharva Kelkar, Aleksander Durumeric, Yaoyi Chen, Frank Noé
2024	J	`jnl`	Artificially intelligent Maxwell's demon for optimal control of open quantum systems. CoRR Paolo Andrea Erdman, Robert Czupryniak, Bibek Bhandari, Andrew N. Jordan, Frank Noé, Jens Eisert, Giacomo Guarnieri
2024	J	`jnl`	Author Correction: Predicting equilibrium distributions for molecular systems with deep learning. Nat. Mac. Intell. 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
2024	A*	`conf`	Doob's Lagrangian: A Sample-Efficient Variational Approach to Transition Path Sampling. NeurIPS Yuanqi Du, Michael Plainer, Rob Brekelmans, Chenru Duan, Frank Noé, Carla P. Gomes, Alán Aspuru-Guzik, Kirill Neklyudov
2024	J	`jnl`	Doob's Lagrangian: A Sample-Efficient Variational Approach to Transition Path Sampling. CoRR Yuanqi Du, Michael Plainer, Rob Brekelmans, Chenru Duan, Frank Noé, Carla P. Gomes, Alán Aspuru-Guzik, Kirill Neklyudov
2024	J	`jnl`	Efficient mapping of phase diagrams with conditional Boltzmann Generators. Mach. Learn. Sci. Technol. Maximilian Schebek, Michele Invernizzi, Frank Noé, Jutta Rogal
2024	J	`jnl`	Efficient mapping of phase diagrams with conditional normalizing flows. CoRR Maximilian Schebek, Michele Invernizzi, Frank Noé, Jutta Rogal
2024	J	`jnl`	Highly Accurate Real-space Electron Densities with Neural Networks. CoRR Lixue Cheng, P. Bernát Szabó, Zeno Schätzle, Derk P. Kooi, Jonas Köhler, Klaas J. H. Giesbertz, Frank Noé, Jan Hermann, Paola Gori-Giorgi, Adam Foster
2024	J	`jnl`	Improved motif-scaffolding with SE(3) flow matching. CoRR 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
2024	J	`jnl`	Improved motif-scaffolding with SE(3) flow matching. Trans. Mach. Learn. Res. 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
2024	J	`jnl`	Improved protein complex prediction with AlphaFold-multimer by denoising the MSA profile. PLoS Comput. Biol. Patrick Bryant, Frank Noé
2024	A*	`conf`	Navigating the Design Space of Equivariant Diffusion-Based Generative Models for De Novo 3D Molecule Generation. ICLR Tuan Le, Julian Cremer, Frank Noé, Djork-Arné Clevert, Kristof T. Schütt
2024	J	`jnl`	PILOT: Equivariant diffusion for pocket conditioned de novo ligand generation with multi-objective guidance via importance sampling. CoRR Julian Cremer, Tuan Le, Frank Noé, Djork-Arné Clevert, Kristof T. Schütt
2024	J	`jnl`	Predicting equilibrium distributions for molecular systems with deep learning. Nat. Mac. Intell. 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
2024	A*	`conf`	Transferable Boltzmann Generators. NeurIPS Leon Klein, Frank Noé
2024	J	`jnl`	Transferable Boltzmann Generators. CoRR Leon Klein, Frank Noé
2023	A*	`conf`	Equivariant flow matching. NeurIPS Leon Klein, Andreas Krämer, Frank Noé
2023	J	`jnl`	Equivariant flow matching. CoRR Leon Klein, Andreas Krämer, Frank Noé
2023	J	`jnl`	Navigating the Design Space of Equivariant Diffusion-Based Generative Models for De Novo 3D Molecule Generation. CoRR Tuan Le, Julian Cremer, Frank Noé, Djork-Arné Clevert, Kristof Schütt
2023	J	`jnl`	Reaction coordinate flows for model reduction of molecular kinetics. CoRR Hao Wu, Frank Noé
2023	A*	`conf`	Rigid Body Flows for Sampling Molecular Crystal Structures. ICML Jonas Köhler, Michele Invernizzi, Pim de Haan, Frank Noé
2023	J	`jnl`	Rigid body flows for sampling molecular crystal structures. CoRR Jonas Köhler, Michele Invernizzi, Pim de Haan, Frank Noé
2023	A*	`conf`	Timewarp: Transferable Acceleration of Molecular Dynamics by Learning Time-Coarsened Dynamics. NeurIPS Leon Klein, Andrew Y. K. Foong, Tor Erlend Fjelde, Bruno Mlodozeniec, Marc Brockschmidt, Sebastian Nowozin, Frank Noé, Ryota Tomioka
2023	J	`jnl`	Timewarp: Transferable Acceleration of Molecular Dynamics by Learning Time-Coarsened Dynamics. CoRR Leon Klein, Andrew Y. K. Foong, Tor Erlend Fjelde, Bruno Mlodozeniec, Marc Brockschmidt, Sebastian Nowozin, Frank Noé, Ryota Tomioka
2023	J	`jnl`	Towards Predicting Equilibrium Distributions for Molecular Systems with Deep Learning. CoRR 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
2023	J	`jnl`	Two for One: Diffusion Models and Force Fields for Coarse-Grained Molecular Dynamics. CoRR Marloes Arts, Victor Garcia Satorras, Chin-Wei Huang, Daniel Zügner, Marco Federici, Cecilia Clementi, Frank Noé, Robert Pinsler, Rianne van den Berg
2022	J	`jnl`	Ab-initio quantum chemistry with neural-network wavefunctions. CoRR Jan Hermann, James S. Spencer, Kenny Choo, Antonio Mezzacapo, W. Matthew C. Foulkes, David Pfau, Giuseppe Carleo, Frank Noé
2022	J	`jnl`	Deeptime: a Python library for machine learning dynamical models from time series data. Mach. Learn. Sci. Technol. 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
2022	J	`jnl`	Driving black-box quantum thermal machines with optimal power/efficiency trade-offs using reinforcement learning. CoRR Paolo Andrea Erdman, Frank Noé
2022	J	`jnl`	Equivariant Graph Attention Networks for Molecular Property Prediction. CoRR Tuan Le, Frank Noé, Djork-Arné Clevert
2022	J	`jnl`	Force-matching Coarse-Graining without Forces. CoRR Jonas Köhler, Yaoyi Chen, Andreas Krämer, Cecilia Clementi, Frank Noé
2022	J	`jnl`	Generating stable molecules using imitation and reinforcement learning. Mach. Learn. Sci. Technol. Søren Ager Meldgaard, Jonas Köhler, Henrik Lund Mortensen, Mads-Peter V. Christiansen, Frank Noé, Bjørk Hammer
2022	J	`jnl`	Machine Learning Coarse-Grained Potentials of Protein Thermodynamics. CoRR Maciej Majewski, Adrià Pérez, Philipp Thölke, Stefan Doerr, Nicholas E. Charron, Toni Giorgino, Brooke E. Husic, Cecilia Clementi, Frank Noé, Gianni De Fabritiis
2022	J	`jnl`	Machine learning frontier orbital energies of nanodiamonds. CoRR Thorren Kirschbaum, Börries von Seggern, Joachim Dzubiella, Annika Bande, Frank Noé
2022	—	`conf`	Representation Learning on Biomolecular Structures Using Equivariant Graph Attention. LoG Tuan Le, Frank Noé, Djork-Arné Clevert
2022	J	`jnl`	Simulation of ligand dissociation kinetics from the protein kinase PYK2. J. Comput. Chem. Justin Spiriti, Frank Noé, Chung F. Wong
2022	A*	`conf`	Unsupervised Learning of Group Invariant and Equivariant Representations. NeurIPS Robin Winter, Marco Bertolini, Tuan Le, Frank Noé, Djork-Arné Clevert
2022	J	`jnl`	Unsupervised Learning of Group Invariant and Equivariant Representations. CoRR Robin Winter, Marco Bertolini, Tuan Le, Frank Noé, Djork-Arné Clevert
2021	J	`jnl`	Auto-Encoding Molecular Conformations. CoRR Robin Winter, Frank Noé, Djork-Arné Clevert
2021	J	`jnl`	Coupling Particle-Based Reaction-Diffusion Simulations with Reservoirs Mediated by Reaction-Diffusion PDEs. Multiscale Model. Simul. Margarita Kostré, Christof Schütte, Frank Noé, Mauricio J. Del Razo
2021	J	`jnl`	Deeptime: a Python library for machine learning dynamical models from time series data. CoRR 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
2021	J	`jnl`	Generating stable molecules using imitation and reinforcement learning. CoRR Søren Ager Meldgaard, Jonas Köhler, Henrik Lund Mortensen, Mads-Peter V. Christiansen, Frank Noé, Bjørk Hammer
2021	J	`jnl`	Identifying optimal cycles in quantum thermal machines with reinforcement-learning. CoRR Paolo Andrea Erdman, Frank Noé
2021	—	`conf`	Parameterized Hypercomplex Graph Neural Networks for Graph Classification. ICANN (3) Tuan Le, Marco Bertolini, Frank Noé, Djork-Arné Clevert
2021	J	`jnl`	Parameterized Hypercomplex Graph Neural Networks for Graph Classification. CoRR Tuan Le, Marco Bertolini, Frank Noé, Djork-Arné Clevert
2021	A*	`conf`	Permutation-Invariant Variational Autoencoder for Graph-Level Representation Learning. NeurIPS Robin Winter, Frank Noé, Djork-Arné Clevert
2021	J	`jnl`	Permutation-Invariant Variational Autoencoder for Graph-Level Representation Learning. CoRR Robin Winter, Frank Noé, Djork-Arné Clevert
2021	A*	`conf`	Smooth Normalizing Flows. NeurIPS Jonas Köhler, Andreas Krämer, Frank Noé
2021	J	`jnl`	Smooth Normalizing Flows. CoRR Jonas Köhler, Andreas Krämer, Frank Noé
2021	J	`jnl`	Symmetric and antisymmetric kernels for machine learning problems in quantum physics and chemistry. Mach. Learn. Sci. Technol. Stefan Klus, Patrick Gelß, Feliks Nüske, Frank Noé
2020	J	`jnl`	Convergence to the fixed-node limit in deep variational Monte Carlo. CoRR Zeno Schätzle, Jan Hermann, Frank Noé
2020	—	`conf`	Deep learning Markov and Koopman models with physical constraints. MSML Andreas Mardt, Luca Pasquali, Frank Noé, Hao Wu
2020	A*	`conf`	Equivariant Flows: Exact Likelihood Generative Learning for Symmetric Densities. ICML Jonas Köhler, Leon Klein, Frank Noé
2020	J	`jnl`	Equivariant Flows: exact likelihood generative learning for symmetric densities. CoRR Jonas Köhler, Leon Klein, Frank Noé
2020	J	`jnl`	Relevance of Rotationally Equivariant Convolutions for Predicting Molecular Properties. CoRR Benjamin Kurt Miller, Mario Geiger, Tess E. Smidt, Frank Noé
2020	A*	`conf`	Stochastic Normalizing Flows. NeurIPS Hao Wu, Jonas Köhler, Frank Noé
2020	J	`jnl`	Stochastic Normalizing Flows. CoRR Hao Wu, Jonas Köhler, Frank Noé
2020	J	`jnl`	TorchMD: A deep learning framework for molecular simulations. CoRR Stefan Doerr, Maciej Majewski, Adrià Pérez, Andreas Krämer, Cecilia Clementi, Frank Noé, Toni Giorgino, Gianni De Fabritiis
2020	J	`jnl`	Training Neural Networks with Property-Preserving Parameter Perturbations. CoRR Andreas Krämer, Jonas Köhler, Frank Noé
2020	J	`jnl`	Variational Approach for Learning Markov Processes from Time Series Data. J. Nonlinear Sci. Hao Wu, Frank Noé
2020	J	`jnl`	grünifai: interactive multiparameter optimization of molecules in a continuous vector space. Bioinform. Robin Winter, Joren Sebastian Retel, Frank Noé, Djork-Arné Clevert, Andreas Steffen
2019	J	`jnl`	Deep neural network solution of the electronic Schrödinger equation. CoRR Jan Hermann, Zeno Schätzle, Frank Noé
2019	J	`jnl`	Equivariant Flows: sampling configurations for multi-body systems with symmetric energies. CoRR Jonas Köhler, Leon Klein, Frank Noé
2019	J	`jnl`	Generating valid Euclidean distance matrices. CoRR Moritz Hoffmann, Frank Noé
2019	J	`jnl`	Machine learning for molecular simulation. CoRR Frank Noé, Alexandre Tkatchenko, Klaus-Robert Müller, Cecilia Clementi
2019	—	`conf`	Porting Adaptive Ensemble Molecular Dynamics Workflows to the Summit Supercomputer. ISC Workshops John R. Ossyra, Ada Sedova, Arnold N. Tharrington, Frank Noé, Cecilia Clementi, Jeremy C. Smith
2019	J	`jnl`	ReaDDy 2: Fast and flexible software framework for interacting-particle reaction dynamics. PLoS Comput. Biol. Moritz Hoffmann, Christoph Fröhner, Frank Noé
2018	J	`jnl`	Boltzmann Generators - Sampling Equilibrium States of Many-Body Systems with Deep Learning. CoRR Frank Noé, Hao Wu
2018	J	`jnl`	Data-Driven Model Reduction and Transfer Operator Approximation. J. Nonlinear Sci. Stefan Klus, Feliks Nüske, Péter Koltai, Hao Wu, Ioannis G. Kevrekidis, Christof Schütte, Frank Noé
2018	A*	`conf`	Deep Generative Markov State Models. NeurIPS Hao Wu, Andreas Mardt, Luca Pasquali, Frank Noé
2018	J	`jnl`	Deep Generative Markov State Models. CoRR Hao Wu, Andreas Mardt, Luca Pasquali, Frank Noé
2018	J	`jnl`	Machine Learning for Molecular Dynamics on Long Timescales. CoRR Frank Noé
2018	J	`jnl`	Machine Learning of coarse-grained Molecular Dynamics Force Fields. CoRR Jiang Wang, Christoph Wehmeyer, Frank Noé, Cecilia Clementi
2018	J	`jnl`	Optimal Data-Driven Estimation of Generalized Markov State Models for Non-Equilibrium Dynamics. Comput. Péter Koltai, Hao Wu, Frank Noé, Christof Schütte
2017	J	`jnl`	Time-lagged autoencoders: Deep learning of slow collective variables for molecular kinetics. CoRR Christoph Wehmeyer, Frank Noé
2016	—	`conf`	Spectral Learning of Dynamic Systems from Nonequilibrium Data. NIPS Hao Wu, Frank Noé
2016	J	`jnl`	Spectral learning of dynamic systems from nonequilibrium data. CoRR Hao Wu, Frank Noé
2015	J	`jnl`	Dynamical Organization of Syntaxin-1A at the Presynaptic Active Zone. PLoS Comput. Biol. Alexander Ullrich, Mathias A. Böhme, Johannes Schöneberg, Harald Depner, Stephan J. Sigrist, Frank Noé
2014	J	`jnl`	Optimal Estimation of Free Energies and Stationary Densities from Multiple Biased Simulations. Multiscale Model. Simul. Hao Wu, Frank Noé
2013	J	`jnl`	A Variational Approach to Modeling Slow Processes in Stochastic Dynamical Systems. Multiscale Model. Simul. Frank Noé, Feliks Nüske
2013	J	`jnl`	Spatiotemporal control of endocytosis by phosphatidylinositol-3, 4-bisphosphate. Nat. 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
2011	Misc	`conf`	A flat Dirichlet process switching model for Bayesian estimation of hybrid systems. ICCS Hao Wu, Frank Noé
2011	J	`jnl`	Efficient Computation, Sensitivity, and Error Analysis of Committor Probabilities for Complex Dynamical Processes. Multiscale Model. Simul. Jan-Hendrik Prinz, Martin Held, Jeremy C. Smith, Frank Noé
2010	Misc	`conf`	Maximum a posteriori estimation for Markov chains based on Gaussian Markov random fields. ICCS Hao Wu, Frank Noé
2010	J	`jnl`	On the Approximation Quality of Markov State Models. Multiscale Model. Simul. Marco Sarich, Frank Noé, Christof Schütte
2010	J	`jnl`	Probability Distance Based Compression of Hidden Markov Models. Multiscale Model. Simul. Hao Wu, Frank Noé
2007	J	`jnl`	A Comparative Study of Clustering Methods for Molecular Data. Int. J. Neural Syst. Lin Wang, Minghu Jiang, Yinghua Lu, Minfu Sun, Frank Noé
2007	—	`conf`	Optimizing in Graphs with Expensive Computation of Edge Weights. OR Frank Noé, Marcus Oswald, Gerhard Reinelt
2006	—	`conf`	Clustering Analysis of Competitive Learning Network for Molecular Data. ISNN (1) Lin Wang, Minghu Jiang, Yinghua Lu, Frank Noé, Jeremy C. Smith
2006	J	`jnl`	Computing Best Transition Pathways in High-Dimensional Dynamical Systems: Application to the Multiscale Model. Simul. Frank Noé, Marcus Oswald, Gerhard Reinelt, Stefan Fischer, Jeremy C. Smith
2006	—	`conf`	Self-Organizing Map Clustering Analysis for Molecular Data. ISNN (1) Lin Wang, Minghu Jiang, Yinghua Lu, Frank Noé, Jeremy C. Smith