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Molecular dynamics (MD) simulation techniques are widely used for various natural science applications.
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Understanding molecular simulation: from algorithms to applications , volume 1
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Evaluation and reparametrization of the opls-aa force field for proteins via comparison with accurate quantum chemical calculations on peptides
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William L Jorgensen and Julian Tirado-Rives · 2005
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Flexible simple point-charge water model with improved liquid-state properties
Yujie Wu, Harald L. Tepper, and Gregory A. Voth · 2006
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Generalized neural-network representation of high-dimensional potential-energy surfaces
Jörg Behler and Michele Parrinello · 2007
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Kinetics from implicit solvent simulations of biomolecules as a function of viscosity
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Molecular modeling and simulation: an interdisciplinary guide , volume 2
Tamar Schlick · 2010
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Statistical mechanics: theory and molecular simulation
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Markov state model reveals folding and functional dynamics in ultra-long md trajectories
Thomas J Lane, Gregory R Bowman, Kyle Beauchamp, Vincent A Voelz, and Vijay S Pande · 2011
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How fast-folding proteins fold
Kresten Lindorff-Larsen, Stefano Piana, Ron O Dror, and David E Shaw · 2011
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PLUMED 2: New feathers for an old bird
Gareth A. Tribello, Massimiliano Bonomi, Davide Branduardi, Carlo Camilloni, and Giovanni Bussi · 2013
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Quantum chemistry structures and properties of 134 kilo molecules
Raghunathan Ramakrishnan, Pavlo O Dral, Matthias Rupp, and O Anatole Von Lilienfeld · 2014
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GROMACS: High performance molecular simulations through multi-level parallelism from laptops to supercomputers
Mark James Abraham, Teemu Murtola, Roland Schulz, Szilárd Páll, Jeremy C. Smith, Berk Hess, and Erik Lindahl · 2015
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Systematic computational and experimental investigation of lithium-ion transport mechanisms in polyester-based polymer electrolytes
Michael A Webb, Yukyung Jung, Danielle M Pesko, Brett M Savoie, Umi Yamamoto, Geoffrey W Coates, Nitash P Balsara, Zhen-Gang Wang, and Thomas F Miller III · 2015
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Amp: A modular approach to machine learning in atomistic simulations
Alireza Khorshidi and Andrew A Peterson · 2016
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Efficient and accurate machine-learning interpolation of atomic energies in compositions with many species
Nongnuch Artrith, Alexander Urban, and Gerbrand Ceder · 2017
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Machine learning of accurate energy-conserving molecular force fields
Stefan Chmiela, Alexandre Tkatchenko, Huziel E Sauceda, Igor Poltavsky, Kristof T Schütt, and Klaus-Robert Müller · 2017
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Openmm 7: Rapid development of high performance algorithms for molecular dynamics
Peter Eastman, Jason Swails, John D Chodera, Robert T McGibbon, Yutong Zhao, Kyle A Beauchamp, Lee-Ping Wang, Andrew C Simmonett, Matthew P Harrigan, Chaya D Stern, et al · 2017
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Prediction errors of molecular machine learning models lower than hybrid dft error
Felix A Faber, Luke Hutchison, Bing Huang, Justin Gilmer, Samuel S Schoenholz, George E Dahl, Oriol Vinyals, Steven Kearnes, Patrick F Riley, and O Anatole Von Lilienfeld · 2017
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Neural message passing for quantum chemistry
Justin Gilmer, Samuel S Schoenholz, Patrick F Riley, Oriol Vinyals, and George E Dahl · 2017
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The atomic simulation environment—a python library for working with atoms
Ask Hjorth Larsen, Jens Jørgen Mortensen, Jakob Blomqvist, Ivano E Castelli, Rune Christensen, Marcin Dułak, Jesper Friis, Michael N Groves, Bjørk Hammer, Cory Hargus, Eric D Hermes, Paul C Jennings, Peter Bjerre Jensen, James Kermode, John R Kitchin, Esben Leonhard Kolsbjerg, Joseph Kubal, Kristen Kaasbjerg, Steen Lysgaard, Jón Bergmann Maronsson, Tristan Maxson, Thomas Olsen, Lars Pastewka, Andrew Peterson, Carsten Rostgaard, Jakob Schiøtz, Ole Schütt, Mikkel Strange, Kristian S Thygesen, Tejs Vegge, Lasse Vilhelmsen, Michael Walter, Zhenhua Zeng, and Karsten W Jacobsen · 2017
Spherical message passing for 3d molecular graphs
Yi Liu, Limei Wang, Meng Liu, Yuchao Lin, Xuan Zhang, Bora Oztekin, and Shuiwang Ji · 2021
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Accurate and scalable graph neural network force field and molecular dynamics with direct force architecture
Cheol Woo Park, Mordechai Kornbluth, Jonathan Vandermause, Chris Wolverton, Boris Kozinsky, and Jonathan P Mailoa · 2021
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Unite: Unitary n-body tensor equivariant network with applications to quantum chemistry
Zhuoran Qiao, Anders S Christensen, Matthew Welborn, Frederick R Manby, Anima Anandkumar, and Thomas F Miller III · 2021
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Modeling of peptides with classical and novel machine learning force fields: A comparison
David Rosenberger, Justin S Smith, and Angel E Garcia · 2021
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E (n) equivariant graph neural networks
Vıctor Garcia Satorras, Emiel Hoogeboom, and Max Welling · 2021
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Stochastic neural network approach for learning high-dimensional free energy surfaces
Elia Schneider, Luke Dai, Robert Q Topper, Christof Drechsel-Grau, and Mark E Tuckerman · 2017
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Schnet: A continuous-filter convolutional neural network for modeling quantum interactions
Kristof Schütt, Pieter-Jan Kindermans, Huziel Enoc Sauceda Felix, Stefan Chmiela, Alexandre Tkatchenko, and Klaus-Robert Müller · 2017
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Ani-1: an extensible neural network potential with dft accuracy at force field computational cost
Justin S Smith, Olexandr Isayev, and Adrian E Roitberg · 2017
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Learning protein structure with a differentiable simulator
John Ingraham, Adam Riesselman, Chris Sander, and Debora Marks · 2018
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Transferable neural networks for enhanced sampling of protein dynamics
Mohammad M Sultan, Hannah K Wayment-Steele, and Vijay S Pande · 2018
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Tensor field networks: Rotation-and translation-equivariant neural networks for 3d point clouds
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A reactive, scalable, and transferable model for molecular energies from a neural network approach based on local information
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Equivariant message passing for the prediction of tensorial properties and molecular spectra
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Differentiable sampling of molecular geometries with uncertainty-based adversarial attacks
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Learning neural network potentials from experimental data via differentiable trajectory reweighting
Stephan Thaler and Julija Zavadlav · 2021
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Equivariant transformers for neural network based molecular potentials
Philipp Thölke and Gianni De Fabritiis · 2021
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When do short-range atomistic machine-learning models fall short?
Shuwen Yue, Maria Carolina Muniz, Marcos F. Calegari Andrade, Linfeng Zhang, Roberto Car, and Athanassios Z. Panagiotopoulos · 2021
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The design space of e (3)-equivariant atom-centered interatomic potentials
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Spice, a dataset of drug-like molecules and peptides for training machine learning potentials, 2022
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