VAMPnets: Deep learning of molecular kinetics

16 October 2017

Frank Noé

Papers citing "VAMPnets: Deep learning of molecular kinetics"

50 / 52 papers shown

Title
Machine Learning of Slow Collective Variables and Enhanced Sampling via Spatial Techniques Tuğçe Gökdemir Jakub Rydzewski 31 1 0 31 Dec 2024
Balanced Neural ODEs: nonlinear model order reduction and Koopman operator approximations Julius Aka Johannes Brunnemann Jörg Eiden Arne Speerforck Lars Mikelsons 31 0 0 14 Oct 2024
MetaGFN: Exploring Distant Modes with Adapted Metadynamics for Continuous GFlowNets Dominic Phillips F. Cipcigan 44 3 0 28 Aug 2024
From Biased to Unbiased Dynamics: An Infinitesimal Generator Approach Timothée Devergne Vladimir Kostic Michele Parrinello Massimiliano Pontil 32 3 0 13 Jun 2024
Foundation Inference Models for Markov Jump Processes David Berghaus K. Cvejoski Patrick Seifner C. Ojeda Ramses J. Sanchez 42 1 0 10 Jun 2024
Dynamical systems and complex networks: A Koopman operator perspective Stefan Klus Natavsa Djurdjevac Conrad 16 2 0 14 May 2024
Koopman-Assisted Reinforcement Learning Preston Rozwood Edward Mehrez Ludger Paehler Wen Sun Steven L. Brunton 40 6 0 04 Mar 2024
Hierarchical deep learning-based adaptive time-stepping scheme for multiscale simulations Asif Hamid Danish Rafiq S. A. Nahvi M. A. Bazaz AI4CE 42 1 0 10 Nov 2023
Deep Learning for Structure-Preserving Universal Stable Koopman-Inspired Embeddings for Nonlinear Canonical Hamiltonian Dynamics P. Goyal Süleyman Yıldız P. Benner 44 3 0 26 Aug 2023
PyKoopman: A Python Package for Data-Driven Approximation of the Koopman Operator Shaowu Pan E. Kaiser Brian M. de Silva J. Nathan Kutz Steven L. Brunton 21 8 0 22 Jun 2023
Neural Markov Jump Processes Patrick Seifner Ramses J. Sanchez BDL 35 7 0 31 May 2023
Implicit Transfer Operator Learning: Multiple Time-Resolution Surrogates for Molecular Dynamics M. Schreiner Ole Winther Simon Olsson OOD AI4CE 46 13 0 29 May 2023
Machine Learning for Partial Differential Equations Steven L. Brunton J. Nathan Kutz AI4CE 45 20 0 30 Mar 2023
Fast conformational clustering of extensive molecular dynamics simulation data Simon Hunkler K. Diederichs O. Kukharenko Christine Peter 15 9 0 11 Jan 2023
SchNetPack 2.0: A neural network toolbox for atomistic machine learning Kristof T. Schütt Stefaan S. P. Hessmann Niklas W. A. Gebauer Jonas Lederer M. Gastegger 25 59 0 11 Dec 2022
DLKoopman: A deep learning software package for Koopman theory Sourya Dey Eric K. Davis AI4CE 21 3 0 15 Nov 2022
Rethinking skip connection model as a learnable Markov chain Dengsheng Chen Jie Hu Wenwen Qiang Xiaoming Wei Enhua Wu BDL 21 1 0 30 Sep 2022
Algorithmic Differentiation for Automated Modeling of Machine Learned Force Fields Niklas Schmitz Klaus-Robert Muller Stefan Chmiela AI4CE 29 11 0 25 Aug 2022
Learning Dynamical Systems via Koopman Operator Regression in Reproducing Kernel Hilbert Spaces Vladimir Kostic P. Novelli Andreas Maurer C. Ciliberto Lorenzo Rosasco Massimiliano Pontil 30 60 0 27 May 2022
Learning Geometrically Disentangled Representations of Protein Folding Simulations N. Joseph Tatro Payel Das Pin-Yu Chen Vijil Chenthamarakshan Rongjie Lai AI4CE 27 0 0 20 May 2022
Characterizing metastable states with the help of machine learning P. Novelli L. Bonati Massimiliano Pontil Michele Parrinello 16 22 0 15 Apr 2022
Discovering Governing Equations from Partial Measurements with Deep Delay Autoencoders Joseph Bakarji Kathleen P. Champion J. Nathan Kutz Steven L. Brunton 40 82 0 13 Jan 2022
GraphVAMPNet, using graph neural networks and variational approach to markov processes for dynamical modeling of biomolecules Mahdi Ghorbani Samarjeet Prasad Jeffery B. Klauda B. Brooks GNN 24 30 0 12 Jan 2022
Rigorous data-driven computation of spectral properties of Koopman operators for dynamical systems Matthew J. Colbrook Alex Townsend 26 67 0 29 Nov 2021
Deeptime: a Python library for machine learning dynamical models from time series data Moritz Hoffmann Martin K. Scherer Tim Hempel Andreas Mardt Brian M. de Silva ... Stefan Klus Hao Wu N. Kutz Steven L. Brunton Frank Noé AI4CE 33 101 0 28 Oct 2021
Learning Stable Koopman Embeddings Fletcher Fan Bowen Yi D. Rye Guodong Shi I. Manchester 33 33 0 13 Oct 2021
Extended dynamic mode decomposition with dictionary learning using neural ordinary differential equations H. Terao Sho Shirasaka Hideyuki Suzuki 26 6 0 01 Oct 2021
Deep Learning Enhanced Dynamic Mode Decomposition D. J. Alford-Lago C. Curtis Alexander T. Ihler Opal Issan 32 34 0 10 Aug 2021
Chasing Collective Variables using Autoencoders and biased trajectories Zineb Belkacemi P. Gkeka T. Lelièvre G. Stoltz AI4CE 6 60 0 22 Apr 2021
Coupling streaming AI and HPC ensembles to achieve 100-1000x faster biomolecular simulations Alexander Brace I. Yakushin Heng Ma Anda Trifan T. Munson Ian Foster A. Ramanathan Hyungro Lee Matteo Turilli S. Jha AI4CE 27 19 0 10 Apr 2021
Modern Koopman Theory for Dynamical Systems Steven L. Brunton M. Budišić E. Kaiser J. Nathan Kutz AI4CE 46 392 0 24 Feb 2021
CKNet: A Convolutional Neural Network Based on Koopman Operator for Modeling Latent Dynamics from Pixels Yongqian Xiao Xin Xu Yifei Shi 16 9 0 19 Feb 2021
Artificial intelligence techniques for integrative structural biology of intrinsically disordered proteins A. Ramanathan Henglong Ma Akash Parvatikar C. Chennubhotla AI4CE 23 40 0 01 Dec 2020
Ensemble Learning of Coarse-Grained Molecular Dynamics Force Fields with a Kernel Approach Jiang Wang Stefan Chmiela K. Müller Frank Noè C. Clementi 8 46 0 04 May 2020
A Perspective on Deep Learning for Molecular Modeling and Simulations Jun Zhang Yao-Kun Lei Zhen Zhang Junhan Chang Maodong Li Xu Han Lijiang Yang Yuqing Yang Y. Gao AI4CE 37 8 0 25 Apr 2020
Autonomous discovery in the chemical sciences part II: Outlook Connor W. Coley Natalie S. Eyke K. Jensen 29 171 0 30 Mar 2020
Methods to Recover Unknown Processes in Partial Differential Equations Using Data Zhen Chen Kailiang Wu D. Xiu 22 3 0 05 Mar 2020
Differentiable Molecular Simulations for Control and Learning Wujie Wang Simon Axelrod Rafael Gómez-Bombarelli AI4CE 109 49 0 27 Feb 2020
Interpretable Embeddings From Molecular Simulations Using Gaussian Mixture Variational Autoencoders Yasemin Bozkurt Varolgunes T. Bereau J. F. Rudzinski DRL 12 42 0 22 Dec 2019
Machine learning for molecular simulation Frank Noé A. Tkatchenko K. Müller C. Clementi AI4CE 16 642 0 07 Nov 2019
Deep Learning Models for Global Coordinate Transformations that Linearize PDEs Craig Gin Bethany Lusch Steven L. Brunton J. Nathan Kutz 13 39 0 07 Nov 2019
Data-driven approximation of the Koopman generator: Model reduction, system identification, and control Stefan Klus Feliks Nuske Sebastian Peitz Jan-Hendrik Niemann C. Clementi Christof Schütte 23 221 0 23 Sep 2019
Capabilities and Limitations of Time-lagged Autoencoders for Slow Mode Discovery in Dynamical Systems Wei Chen Hythem Sidky Andrew L. Ferguson 25 36 0 02 Jun 2019
Structure-preserving Method for Reconstructing Unknown Hamiltonian Systems from Trajectory Data Kailiang Wu Tong Qin D. Xiu 21 31 0 24 May 2019
Dimensionality Reduction of Complex Metastable Systems via Kernel Embeddings of Transition Manifolds A. Bittracher Stefan Klus B. Hamzi P. Koltai Christof Schütte 19 22 0 18 Apr 2019
Time Series Source Separation using Dynamic Mode Decomposition Arvind Prasadan R. Nadakuditi AI4TS 21 6 0 04 Mar 2019
Machine Learning for Molecular Dynamics on Long Timescales Frank Noé AI4CE 23 32 0 18 Dec 2018
Data Driven Governing Equations Approximation Using Deep Neural Networks Tong Qin Kailiang Wu D. Xiu PINN 34 270 0 13 Nov 2018
Deep learning for universal linear embeddings of nonlinear dynamics Bethany Lusch J. Nathan Kutz Steven L. Brunton 27 1,218 0 27 Dec 2017
Variational Encoding of Complex Dynamics Carlos X. Hernández H. Wayment-Steele Mohammad M. Sultan B. Husic Vijay S. Pande AI4CE 25 138 0 23 Nov 2017