Clustering Analysis of Competitive Learning Network for Molecular Data. - Lin Wang 006; Minghu Jiang; Yinghua Lu; Frank Noé; Jeremy C. Smith (ISNN (1), 2006)

Self-Organizing Map Clustering Analysis for Molecular Data. - Lin Wang 006; Minghu Jiang; Yinghua Lu; Frank Noé; Jeremy C. Smith (ISNN (1), 2006)

Computing Best Transition Pathways in High-Dimensional Dynamical Systems: Application to the AlphaL leftrightharpoons Beta leftrightharpoons AlphaR Transitions in Octaalanine. - Frank Noé; Marcus Oswald; Gerhard Reinelt; Stefan Fischer 004; Jeremy C. Smith (Multiscale Model. Simul., 2006)

Optimizing in Graphs with Expensive Computation of Edge Weights. - Frank Noé; Marcus Oswald; Gerhard Reinelt (OR, 2007)

A Comparative Study of Clustering Methods for Molecular Data. - Lin Wang 006; Minghu Jiang; Yinghua Lu; Minfu Sun; Frank Noé (Int. J. Neural Syst., 2007)

On the Approximation Quality of Markov State Models. - Marco Sarich; Frank Noé; Christof Schütte (Multiscale Model. Simul., 2010)

Probability Distance Based Compression of Hidden Markov Models. - Hao Wu 035; Frank Noé (Multiscale Model. Simul., 2010)

Maximum a posteriori estimation for Markov chains based on Gaussian Markov random fields. - Hao Wu 035; Frank Noé (ICCS, 2010)

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)

A flat Dirichlet process switching model for Bayesian estimation of hybrid systems. - Hao Wu 035; Frank Noé (ICCS, 2011)

A Variational Approach to Modeling Slow Processes in Stochastic Dynamical Systems. - Frank Noé; Feliks Nüske (Multiscale Model. Simul., 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; et al. (Nat., 2013)

Optimal Estimation of Free Energies and Stationary Densities from Multiple Biased Simulations. - Hao Wu 035; Frank Noé (Multiscale Model. Simul., 2014)

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)

Spectral Learning of Dynamic Systems from Nonequilibrium Data. - Hao Wu 035; Frank Noé (NIPS, 2016)

Time-lagged autoencoders: Deep learning of slow collective variables for molecular kinetics. - Christoph Wehmeyer; Frank Noé (CoRR, 2017)

Deep Generative Markov State Models. - Hao Wu 035; Andreas Mardt; Luca Pasquali; Frank Noé (NeurIPS, 2018)

Optimal Data-Driven Estimation of Generalized Markov State Models for Non-Equilibrium Dynamics. - Péter Koltai; Hao Wu 035; Frank Noé; Christof Schütte (Comput., 2018)

Boltzmann Generators - Sampling Equilibrium States of Many-Body Systems with Deep Learning. - Frank Noé; Hao Wu 035 (CoRR, 2018)

Machine Learning of coarse-grained Molecular Dynamics Force Fields. - Jiang Wang; Christoph Wehmeyer; Frank Noé; Cecilia Clementi (CoRR, 2018)

Machine Learning for Molecular Dynamics on Long Timescales. - Frank Noé (CoRR, 2018)

Data-Driven Model Reduction and Transfer Operator Approximation. - Stefan Klus; Feliks Nüske; Péter Koltai; Hao Wu 035; Ioannis G. Kevrekidis; Christof Schütte; Frank Noé (J. Nonlinear Sci., 2018)

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 (ISC Workshops, 2019)

Deep neural network solution of the electronic Schrödinger equation. - Jan Hermann; Zeno Schätzle; Frank Noé (CoRR, 2019)

Equivariant Flows: sampling configurations for multi-body systems with symmetric energies. - Jonas Köhler 001; Leon Klein; Frank Noé (CoRR, 2019)

Generating valid Euclidean distance matrices. - Moritz Hoffmann 002; Frank Noé (CoRR, 2019)

Machine learning for molecular simulation. - Frank Noé; Alexandre Tkatchenko; Klaus-Robert Müller; Cecilia Clementi (CoRR, 2019)

ReaDDy 2: Fast and flexible software framework for interacting-particle reaction dynamics. - Moritz Hoffmann 002; Christoph Fröhner; Frank Noé (PLoS Comput. Biol., 2019)

Equivariant Flows: Exact Likelihood Generative Learning for Symmetric Densities. - Jonas Köhler 001; Leon Klein; Frank Noé (ICML, 2020)

Deep learning Markov and Koopman models with physical constraints. - Andreas Mardt; Luca Pasquali; Frank Noé; Hao Wu 035 (MSML, 2020)

Stochastic Normalizing Flows. - Hao Wu 035; Jonas Köhler 001; Frank Noé (NeurIPS, 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)

Relevance of Rotationally Equivariant Convolutions for Predicting Molecular Properties. - Benjamin Kurt Miller; Mario Geiger; Tess E. Smidt; Frank Noé (CoRR, 2020)

Convergence to the fixed-node limit in deep variational Monte Carlo. - Zeno Schätzle; Jan Hermann; Frank Noé (CoRR, 2020)

Training Neural Networks with Property-Preserving Parameter Perturbations. - Andreas Krämer; Jonas Köhler 001; Frank Noé (CoRR, 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)

Variational Approach for Learning Markov Processes from Time Series Data. - Hao Wu 035; Frank Noé (J. Nonlinear Sci., 2020)

Parameterized Hypercomplex Graph Neural Networks for Graph Classification. - Tuan Le; Marco Bertolini; Frank Noé; Djork-Arné Clevert (ICANN (3), 2021)

Smooth Normalizing Flows. - Jonas Köhler 001; Andreas Krämer; Frank Noé (NeurIPS, 2021)

Permutation-Invariant Variational Autoencoder for Graph-Level Representation Learning. - Robin Winter; Frank Noé; Djork-Arné Clevert (NeurIPS, 2021)

Auto-Encoding Molecular Conformations. - Robin Winter; Frank Noé; Djork-Arné Clevert (CoRR, 2021)

Identifying optimal cycles in quantum thermal machines with reinforcement-learning. - Paolo Andrea Erdman; Frank Noé (CoRR, 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)

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)

Representation Learning on Biomolecular Structures Using Equivariant Graph Attention. - Tuan Le; Frank Noé; Djork-Arné Clevert (LoG, 2022)

Unsupervised Learning of Group Invariant and Equivariant Representations. - Robin Winter; Marco Bertolini; Tuan Le; Frank Noé; Djork-Arné Clevert (NeurIPS, 2022)

Equivariant Graph Attention Networks for Molecular Property Prediction. - Tuan Le; Frank Noé; Djork-Arné Clevert (CoRR, 2022)

Force-matching Coarse-Graining without Forces. - Jonas Köhler 001; Yaoyi Chen; Andreas Krämer; Cecilia Clementi; 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)

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)

Machine learning frontier orbital energies of nanodiamonds. - Thorren Kirschbaum; Börries von Seggern; Joachim Dzubiella; Annika Bande; Frank Noé (CoRR, 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)

Simulation of ligand dissociation kinetics from the protein kinase PYK2. - Justin Spiriti; Frank Noé; Chung F. Wong (J. Comput. Chem., 2022)

Deeptime: a Python library for machine learning dynamical models from time series data. - Moritz Hoffmann 002; Martin Scherer; Tim Hempel; Andreas Mardt; Brian de Silva; Brooke E. Husic; Stefan Klus; Hao Wu 035; J. Nathan Kutz; Steven L. Brunton; Frank Noé (Mach. Learn. Sci. Technol., 2022)

Generating stable molecules using imitation and reinforcement learning. - Søren Ager Meldgaard; Jonas Köhler 001; Henrik Lund Mortensen; Mads-Peter V. Christiansen; Frank Noé; Bjørk Hammer (Mach. Learn. Sci. Technol., 2022)

Rigid Body Flows for Sampling Molecular Crystal Structures. - Jonas Köhler 001; Michele Invernizzi; Pim de Haan; Frank Noé (ICML, 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)

Equivariant flow matching. - Leon Klein; Andreas Krämer; Frank Noé (NeurIPS, 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)

Towards Predicting Equilibrium Distributions for Molecular Systems with Deep Learning. - Shuxin Zheng; Jiyan He; Chang Liu 030; Yu Shi; Ziheng Lu; Weitao Feng 001; Fusong Ju; Jiaxi Wang; Jianwei Zhu; Yaosen Min; He Zhang; Shidi Tang; et al. (CoRR, 2023)

Reaction coordinate flows for model reduction of molecular kinetics. - Hao Wu 035; Frank Noé (CoRR, 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 T. Schütt (ICLR, 2024)

Doob's Lagrangian: A Sample-Efficient Variational Approach to Transition Path Sampling. - Yuanqi Du; Michael Plainer; Rob Brekelmans; Chenru Duan; Frank Noé; Carla P. Gomes; Alán Aspuru-Guzik; Kirill Neklyudov (NeurIPS, 2024)

Transferable Boltzmann Generators. - Leon Klein; Frank Noé (NeurIPS, 2024)

PILOT: Equivariant diffusion for pocket conditioned de novo ligand generation with multi-objective guidance via importance sampling. - Julian Cremer; Tuan Le; Frank Noé; Djork-Arné Clevert; Kristof T. Schütt (CoRR, 2024)

Efficient mapping of phase diagrams with conditional normalizing flows. - Maximilian Schebek; Michele Invernizzi; Frank Noé; Jutta Rogal (CoRR, 2024)

Artificially intelligent Maxwell's demon for optimal control of open quantum systems. - Paolo Andrea Erdman; Robert Czupryniak; Bibek Bhandari; Andrew N. Jordan; Frank Noé; Jens Eisert; Giacomo Guarnieri (CoRR, 2024)

Highly Accurate Real-space Electron Densities with Neural Networks. - Lixue Cheng; P. Bernát Szabó; Zeno Schätzle; Derk P. Kooi; Jonas Köhler 001; Klaas J. H. Giesbertz; Frank Noé; Jan Hermann; Paola Gori-Giorgi; Adam Foster 001 (CoRR, 2024)

Predicting equilibrium distributions for molecular systems with deep learning. - Shuxin Zheng; Jiyan He; Chang Liu 030; Yu Shi; Ziheng Lu; Weitao Feng 001; Fusong Ju; Jiaxi Wang; Jianwei Zhu; Yaosen Min; He Zhang; Shidi Tang; et al. (Nat. Mac. Intell., 2024)

Author Correction: Predicting equilibrium distributions for molecular systems with deep learning. - Shuxin Zheng; Jiyan He; Chang Liu 030; Yu Shi; Ziheng Lu; Weitao Feng 001; Fusong Ju; Jiaxi Wang; Jianwei Zhu; Yaosen Min; He Zhang; Shidi Tang; et al. (Nat. Mac. Intell., 2024)

Improved protein complex prediction with AlphaFold-multimer by denoising the MSA profile. - Patrick Bryant; Frank Noé (PLoS Comput. Biol., 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 (Trans. Mach. Learn. Res., 2024)

Ab-initio simulation of excited-state potential energy surfaces with transferable deep quantum Monte Carlo. - Zeno Schätzle; P. Bernát Szabó; Alice Cuzzocrea; Frank Noé (CoRR, 2025)

Consistent Sampling and Simulation: Molecular Dynamics with Energy-Based Diffusion Models. - Michael Plainer; Hao Wu 035; Leon Klein; Stephan Günnemann; Frank Noé (CoRR, 2025)

Operator Forces For Coarse-Grained Molecular Dynamics. - Leon Klein; Atharva Kelkar; Aleksander Durumeric; Yaoyi Chen; Frank Noé (CoRR, 2025)

An ab initio foundation model of wavefunctions that accurately describes chemical bond breaking. - Adam Foster 001; Zeno Schätzle; P. Bernát Szabó; Lixue Cheng; Jonas Köhler 001; Gino Cassella; Nicholas Gao; Jiawei Li; Frank Noé; Jan Hermann (CoRR, 2025)