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Learning neural network potentials from experimental data via Differentiable Trajectory Reweighting

2 June 2021

Papers citing "Learning neural network potentials from experimental data via Differentiable Trajectory Reweighting"

22 / 22 papers shown

Title
JaxSGMC: Modular stochastic gradient MCMC in JAX Stephan Thaler Paul Fuchs Ana Cukarska Julija Zavadlav BDL 203 2 0 16 May 2025
Predicting solvation free energies with an implicit solvent machine learning potential Sebastien Röcken A. F. Burnet Julija Zavadlav AI4Cl AI4CE 155 5 0 31 May 2024
Accelerated Simulations of Molecular Systems through Learning of their Effective Dynamics Pantelis R. Vlachas Julija Zavadlav M. Praprotnik Petros Koumoutsakos AI4CE 54 3 0 17 Feb 2021
Differentiable sampling of molecular geometries with uncertainty-based adversarial attacks Daniel Schwalbe-Koda Aik Rui Tan Rafael Gómez-Bombarelli AAML 72 61 0 27 Jan 2021
TorchMD: A deep learning framework for molecular simulations Stefan Doerr Maciej Majewski Adria Pérez Andreas Krämer C. Clementi Frank Noe T. Giorgino Gianni De Fabritiis AI4CE 135 173 0 22 Dec 2020
Fast and Uncertainty-Aware Directional Message Passing for Non-Equilibrium Molecules Johannes Klicpera Shankari Giri Johannes T. Margraf Stephan Günnemann 79 324 0 28 Nov 2020
Coarse Graining Molecular Dynamics with Graph Neural Networks B. Husic N. Charron Dominik Lemm Jiang Wang Adria Pérez ... Yaoyi Chen Simon Olsson Gianni De Fabritiis Frank Noé C. Clementi AI4CE 98 160 0 22 Jul 2020
OrbNet: Deep Learning for Quantum Chemistry Using Symmetry-Adapted Atomic-Orbital Features Zhuoran Qiao Matthew Welborn Anima Anandkumar F. Manby Thomas F. Miller AI4CE 73 217 0 15 Jul 2020
Directional Message Passing for Molecular Graphs Johannes Klicpera Janek Groß Stephan Günnemann 127 881 0 06 Mar 2020
Differentiable Molecular Simulations for Control and Learning Wujie Wang Simon Axelrod Rafael Gómez-Bombarelli AI4CE 183 49 0 27 Feb 2020
Learning to Control PDEs with Differentiable Physics Philipp Holl V. Koltun Nils Thuerey AI4CE PINN 75 189 0 21 Jan 2020
Machine learning for molecular simulation Frank Noé A. Tkatchenko K. Müller C. Clementi AI4CE 75 664 0 07 Nov 2019
DiffTaichi: Differentiable Programming for Physical Simulation Yuanming Hu Luke Anderson Tzu-Mao Li Qi Sun N. Carr Jonathan Ragan-Kelley F. Durand 65 388 0 01 Oct 2019
A Differentiable Programming System to Bridge Machine Learning and Scientific Computing Mike Innes Alan Edelman Keno Fischer Chris Rackauckas Elliot Saba Viral B. Shah Will Tebbutt PINN 57 184 0 17 Jul 2019
Machine Learning of coarse-grained Molecular Dynamics Force Fields Jiang Wang Simon Olsson C. Wehmeyer Adria Pérez Nicholas E. Charron Gianni De Fabritiis Frank Noe C. Clementi AI4CE 38 405 0 04 Dec 2018
Active Learning of Uniformly Accurate Inter-atomic Potentials for Materials Simulation Linfeng Zhang De-Ye Lin Han Wang R. Car E. Weinan 59 336 0 28 Oct 2018
Neural Ordinary Differential Equations T. Chen Yulia Rubanova J. Bettencourt David Duvenaud AI4CE 434 5,157 0 19 Jun 2018
SchNet: A continuous-filter convolutional neural network for modeling quantum interactions Kristof T. Schütt Pieter-Jan Kindermans Huziel Enoc Sauceda Felix Stefan Chmiela A. Tkatchenko K. Müller 155 1,086 0 26 Jun 2017
Neural Message Passing for Quantum Chemistry Justin Gilmer S. Schoenholz Patrick F. Riley Oriol Vinyals George E. Dahl 598 7,488 0 04 Apr 2017
A Differentiable Physics Engine for Deep Learning in Robotics Jonas Degrave Michiel Hermans J. Dambre Francis Wyffels PINN AI4CE 84 231 0 05 Nov 2016
Automatic differentiation in machine learning: a survey A. G. Baydin Barak A. Pearlmutter Alexey Radul J. Siskind PINN AI4CE ODL 168 2,816 0 20 Feb 2015
Adam: A Method for Stochastic Optimization Diederik P. Kingma Jimmy Ba ODL 2.0K 150,312 0 22 Dec 2014