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Machine Learning of coarse-grained Molecular Dynamics Force Fields

4 December 2018

Frank Noe

Papers citing "Machine Learning of coarse-grained Molecular Dynamics Force Fields"

50 / 73 papers shown

Title
Graph Fourier Neural ODEs: Modeling Spatial-temporal Multi-scales in Molecular Dynamics Fang Sun Zijie Huang Haixin Wang Yadi Cao Xiao Luo Wei Wang Yizhou Sun AI4CE 96 0 0 01 Jul 2025
chemtrain-deploy: A parallel and scalable framework for machine learning potentials in million-atom MD simulations Paul Fuchs Weilong Chen Stephan Thaler Julija Zavadlav 52 0 0 04 Jun 2025
Energy-Based Coarse-Graining in Molecular Dynamics: A Flow-Based Framework Without Data Maximilian Stupp P. S. Koutsourelakis 85 0 0 29 Apr 2025
Graph-Aware Isomorphic Attention for Adaptive Dynamics in Transformers Markus J. Buehler AI4CE 147 3 0 04 Jan 2025
The dark side of the forces: assessing non-conservative force models for atomistic machine learning Filippo Bigi Marcel F. Langer Michele Ceriotti AI4CE 268 12 0 16 Dec 2024
Implicit Delta Learning of High Fidelity Neural Network Potentials Stephan Thaler Cristian Gabellini Nikhil Shenoy Prudencio Tossou AI4CE 153 1 0 08 Dec 2024
Learning Macroscopic Dynamics from Partial Microscopic Observations Mengyi Chen Qianxiao Li AI4CE 71 0 0 31 Oct 2024
EquiJump: Protein Dynamics Simulation via SO(3)-Equivariant Stochastic Interpolants Allan dos Santos Costa Ilan Mitnikov Franco Pellegrini Ameya Daigavane Mario Geiger Zhonglin Cao Karsten Kreis Tess E. Smidt E. Küçükbenli J. Jacobson 67 1 0 12 Oct 2024
AMARO: All Heavy-Atom Transferable Neural Network Potentials of Protein Thermodynamics Antonio Mirarchi Raúl P. Peláez Guillem Simeon Gianni De Fabritiis 91 3 0 26 Sep 2024
On the design space between molecular mechanics and machine learning force fields Yuanqing Wang Kenichiro Takaba Michael S. Chen Marcus Wieder Yuzhi Xu ... Kyunghyun Cho Joe G. Greener Peter K. Eastman Stefano Martiniani M. Tuckerman AI4CE 112 5 0 03 Sep 2024
chemtrain: Learning Deep Potential Models via Automatic Differentiation and Statistical Physics Paul Fuchs Stephan Thaler Sebastien Röcken Julija Zavadlav DiffM 162 10 0 28 Aug 2024
Thermodynamic Transferability in Coarse-Grained Force Fields using Graph Neural Networks Emily Shinkle Aleksandra Pachalieva Riti Bahl Sakib Matin Brendan Gifford G. Craven Nicholas Lubbers 63 3 0 17 Jun 2024
Conditional Normalizing Flows for Active Learning of Coarse-Grained Molecular Representations Henrik Schopmans Pascal Friederich AI4CE 91 2 0 02 Feb 2024
Large Scale Training of Graph Neural Networks for Optimal Markov-Chain Partitioning Using the Kemeny Constant S. Martino João Morado Chenghao Li Zhenghao Lu E. Rosta GNN 18 0 0 22 Dec 2023
Navigating protein landscapes with a machine-learned transferable coarse-grained model N. Charron Felix Musil Andrea Guljas Yaoyi Chen Klara Bonneau ... B. Husic Ankit Patel Gianni De Fabritiis Frank Noé C. Clementi AI4CE 84 14 0 27 Oct 2023
Towards equilibrium molecular conformation generation with GFlowNets Alexandra Volokhova Michal Koziarski Alex Hernández-García Cheng-Hao Liu Santiago Miret Pablo Lemos Luca Thiede Zichao Yan Alán Aspuru-Guzik Yoshua Bengio 75 11 0 20 Oct 2023
Score dynamics: scaling molecular dynamics with picoseconds timestep via conditional diffusion model Tim Hsu Babak Sadigh Vasily V. Bulatov Fei Zhou 58 14 0 02 Oct 2023
Overcoming the Barrier of Orbital-Free Density Functional Theory for Molecular Systems Using Deep Learning He Zhang Siyuan Liu Jiacheng You Chang-Shu Liu Shuxin Zheng Ziheng Lu Tong Wang Nanning Zheng Jia Zhang 65 21 0 28 Sep 2023
Latent Representation and Simulation of Markov Processes via Time-Lagged Information Bottleneck Marco Federici Patrick Forré Ryota Tomioka Bastiaan S. Veeling 51 6 0 13 Sep 2023
DiAMoNDBack: Diffusion-denoising Autoregressive Model for Non-Deterministic Backmapping of Cα Protein Traces Michael S. Jones Kirill Shmilovich Andrew L. Ferguson DiffM 79 14 0 23 Jul 2023
Top-down machine learning of coarse-grained protein force-fields Carles Navarro Maciej Majewski Gianni De Fabritiis AI4CE 96 12 0 20 Jun 2023
Towards Predicting Equilibrium Distributions for Molecular Systems with Deep Learning Shuxin Zheng Jiyan He Chang-Shu Liu Yu Shi Ziheng Lu ... Peiran Jin Chi Chen Frank Noé Haiguang Liu Tie-Yan Liu AI4CE 89 41 0 08 Jun 2023
Str2Str: A Score-based Framework for Zero-shot Protein Conformation Sampling Jiarui Lu Bozitao Zhong Zuobai Zhang Jian Tang 93 32 0 05 Jun 2023
Neural Markov Jump Processes Patrick Seifner Ramses J. Sanchez BDL 81 8 0 31 May 2023
Implicit Transfer Operator Learning: Multiple Time-Resolution Surrogates for Molecular Dynamics Mathias Jacob Schreiner Ole Winther Simon Olsson OOD AI4CE 127 13 0 29 May 2023
FlexVDW: A machine learning approach to account for protein flexibility in ligand docking Patricia Suriana Joseph M. Paggi R. Dror AI4CE 24 1 0 20 Mar 2023
Statistically Optimal Force Aggregation for Coarse-Graining Molecular Dynamics Andreas Krämer Aleksander E. P. Durumeric N. Charron Yaoyi Chen C. Clementi Frank Noé AI4CE 70 22 0 14 Feb 2023
Two for One: Diffusion Models and Force Fields for Coarse-Grained Molecular Dynamics Marloes Arts Victor Garcia Satorras Chin-Wei Huang Daniel Zuegner Marco Federici C. Clementi Frank Noé Robert Pinsler Rianne van den Berg DiffM 111 92 0 01 Feb 2023
Scalable Bayesian Uncertainty Quantification for Neural Network Potentials: Promise and Pitfalls Stephan Thaler Gregor Doehner Julija Zavadlav 101 21 0 15 Dec 2022
Machine Learning Coarse-Grained Potentials of Protein Thermodynamics Maciej Majewski Adriana Pérez Philipp Thölke Stefan Doerr N. Charron T. Giorgino B. Husic C. Clementi Frank Noé Gianni De Fabritiis AI4CE 73 76 0 14 Dec 2022
Forces are not Enough: Benchmark and Critical Evaluation for Machine Learning Force Fields with Molecular Simulations Xiang Fu Zhenghao Wu Wujie Wang T. Xie S. Keten Rafael Gómez-Bombarelli Tommi Jaakkola 90 146 0 13 Oct 2022
Developing Machine-Learned Potentials for Coarse-Grained Molecular Simulations: Challenges and Pitfalls E. Ricci Georgios Paliouras V. Karkaletsis D. Theodorou Niki Vergadou AI4CE 54 9 0 26 Sep 2022
Learning Pair Potentials using Differentiable Simulations Wujie Wang Zhenghao Wu Rafael Gómez-Bombarelli 82 26 0 16 Sep 2022
Graph neural networks for materials science and chemistry Patrick Reiser Marlen Neubert André Eberhard Luca Torresi Chen Zhou ... Houssam Metni Clint van Hoesel Henrik Schopmans T. Sommer Pascal Friederich GNN AI4CE 122 419 0 05 Aug 2022
Flow Annealed Importance Sampling Bootstrap Laurence Illing Midgley Vincent Stimper G. Simm Bernhard Schölkopf José Miguel Hernández-Lobato 136 95 0 03 Aug 2022
Simulate Time-integrated Coarse-grained Molecular Dynamics with Multi-Scale Graph Networks Xiang Fu T. Xie Nathan J. Rebello B. Olsen Tommi Jaakkola AI4CE 75 15 0 21 Apr 2022
Automatic Identification of Chemical Moieties Jonas Lederer M. Gastegger Kristof T. Schütt Michael C. Kampffmeyer Klaus-Robert Muller Oliver T. Unke 69 5 0 30 Mar 2022
Differentiable Matrix Elements with MadJax Lukas Heinrich Michael Kagan 58 20 0 28 Feb 2022
TorchMD-NET: Equivariant Transformers for Neural Network based Molecular Potentials Philipp Thölke Gianni De Fabritiis AI4CE 125 197 0 05 Feb 2022
Learning Physics-Consistent Particle Interactions Zhichao Han David S. Kammer Olga Fink 69 7 0 01 Feb 2022
Generative Coarse-Graining of Molecular Conformations Wujie Wang Minkai Xu Chen Cai Benjamin Kurt Miller Tess E. Smidt Yusu Wang Jian Tang Rafael Gómez-Bombarelli 66 36 0 28 Jan 2022
Graph Neural Networks Accelerated Molecular Dynamics Zijie Li Kazem Meidani Prakarsh Yadav A. Farimani GNN AI4CE 70 59 0 06 Dec 2021
Molecular Dynamics Simulations on Cloud Computing and Machine Learning Platforms Prateek Sharma V. Jadhao AI4CE 8 10 0 11 Nov 2021
Constructing Neural Network-Based Models for Simulating Dynamical Systems Christian Møldrup Legaard Thomas Schranz G. Schweiger Ján Drgovna Basak Falay C. Gomes Alexandros Iosifidis M. Abkar P. Larsen PINN AI4CE 63 98 0 02 Nov 2021
Designing Machine Learning Surrogates using Outputs of Molecular Dynamics Simulations as Soft Labels J. Kadupitiya Nasim Anousheh V. Jadhao AI4CE SyDa 8 0 0 27 Oct 2021
Smooth Normalizing Flows Jonas Köhler Andreas Krämer Frank Noé 105 55 0 01 Oct 2021
Neural Upscaling from Residue-level Protein Structure Networks to Atomistic Structure Vy T Duong Elizabeth M. Diessner Gianmarc Grazioli Rachel W. Martin C. Butts 24 5 0 25 Aug 2021
Physics-Guided Deep Learning for Dynamical Systems: A Survey Rui Wang Rose Yu AI4CE PINN 109 69 0 02 Jul 2021
Learning neural network potentials from experimental data via Differentiable Trajectory Reweighting Stephan Thaler Julija Zavadlav 79 71 0 02 Jun 2021
Accelerated Simulations of Molecular Systems through Learning of their Effective Dynamics Pantelis R. Vlachas Julija Zavadlav M. Praprotnik Petros Koumoutsakos AI4CE 56 3 0 17 Feb 2021