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The need to use a short time step is a key limit on the speed of molecular dynamics (MD) simulations.
Generalized verlet algorithm for efficient molecular dynamics simulations with long-range interactions
Helmut Grubmüller, Helmut Heller, Andreas Windemuth, and Klaus Schulten · 1991
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Molecular dynamics in systems with multiple time scales: Systems with stiff and soft degrees of freedom and with short and long range forces
Mark E Tuckerman and Bruce J Berne · 1991
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Reversible multiple time scale molecular dynamics
MBBJM Tuckerman, Bruce J Berne, and Glenn J Martyna · 1992
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A multiple-time-step molecular dynamics algorithm for macromolecules
Darryl D Humphreys, Richard A Friesner, and Bruce J Berne · 1994
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Development and testing of the opls all-atom force field on conformational energetics and properties of organic liquids
William L Jorgensen, David S Maxwell, and Julian Tirado-Rives · 1996
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Particle-mesh ewald and rrespa for parallel molecular dynamics simulations
Steve Plimpton · 1997
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Long-time-step methods for oscillatory differential equations
Bosco Garcia-Archilla, Jesús Maria Sanz-Serna, and Robert D Skeel · 1998
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Namd2: greater scalability for parallel molecular dynamics
Laxmikant Kalé, Robert Skeel, Milind Bhandarkar, Robert Brunner, Attila Gursoy, Neal Krawetz, James Phillips, Aritomo Shinozaki, Krishnan Varadarajan, and Klaus Schulten · 1999
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Langevin stabilization of molecular dynamics
Jesús A Izaguirre, Daniel P Catarello, Justin M Wozniak, and Robert D Skeel · 2001
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Potential energy functions for atomic-level simulations of water and organic and biomolecular systems
William L Jorgensen and Julian Tirado-Rives · 2005
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The influence of temperature and density functional models in ab initio molecular dynamics simulation of liquid water
Joost VandeVondele, Fawzi Mohamed, Matthias Krack, Jürg Hutter, Michiel Sprik, and Michele Parrinello · 2005
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Scalable algorithms for molecular dynamics simulations on commodity clusters
Kevin J Bowers, Edmond Chow, Huafeng Xu, Ron O Dror, Michael P Eastwood, Brent A Gregersen, John L Klepeis, Istvan Kolossvary, Mark A Moraes, Federico D Sacerdoti, et al · 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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Multiple time step integrators in ab initio molecular dynamics
Nathan Luehr, Thomas E Markland, and Todd J Martínez · 2014
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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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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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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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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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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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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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A versatile multiple time step scheme for efficient ab initio molecular dynamics simulations
Elisa Liberatore, Rocco Meli, and Ursula Rothlisberger · 2018
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A reactive, scalable, and transferable model for molecular energies from a neural network approach based on local information
Oliver T Unke and Markus Meuwly · 2018
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End-to-end symmetry preserving inter-atomic potential energy model for finite and extended systems
Linfeng Zhang, Jiequn Han, Han Wang, Wissam Saidi, Roberto Car, et al · 2018
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Deep potential molecular dynamics: a scalable model with the accuracy of quantum mechanics
Linfeng Zhang, Jiequn Han, Han Wang, Roberto Car, and EJPRL Weinan · 2018
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Spookynet: Learning force fields with electronic degrees of freedom and nonlocal effects
Oliver T Unke, Stefan Chmiela, Michael Gastegger, Kristof T Schütt, Huziel E Sauceda, and Klaus-Robert Müller · 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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Machine learning force fields
Oliver T Unke, Stefan Chmiela, Huziel E Sauceda, Michael Gastegger, Igor Poltavsky, Kristof T Sch utt, Alexandre Tkatchenko, and Klaus-Robert Müller · 2021
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Torchmd: A deep learning framework for molecular simulations
Stefan Doerr, Maciej Majewski, Adrià Pérez, Andreas Kramer, Cecilia Clementi, Frank Noe, Toni Giorgino, and Gianni De Fabritiis · 2021
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LAMMPS - a flexible simulation tool for particle-based materials modeling at the atomic, meso, and continuum scales
A. P. Thompson, H. M. Aktulga, R. Berger, D. S. Bolintineanu, W. M. Brown, P. S. Crozier, P. J. in ’t Veld, A. Kohlmeyer, S. G. Moore, T. D. Nguyen, R. Shan, M. J. Stevens, J. Tranchida, C. Trott, and S. J. Plimpton · 2022
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Accurate and transferable multitask prediction of chemical properties with an atoms-in-molecules neural network
Roman Zubatyuk, Justin S Smith, Jerzy Leszczynski, and Olexandr Isayev · 2019
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Ab initio thermodynamics of liquid and solid water
Bingqing Cheng, Edgar A Engel, Jörg Behler, Christoph Dellago, and Michele Ceriotti · 2019
Cited alongside, same era.
The ani-1ccx and ani-1x data sets, coupled-cluster and density functional theory properties for molecules
Justin S Smith, Roman Zubatyuk, Benjamin Nebgen, Nicholas Lubbers, Kipton Barros, Adrian E Roitberg, Olexandr Isayev, and Sergei Tretiak · 2020
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Extending the applicability of the ani deep learning molecular potential to sulfur and halogens
Christian Devereux, Justin S Smith, Kate K Huddleston, Kipton Barros, Roman Zubatyuk, Olexandr Isayev, and Adrian E Roitberg · 2020
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Directional message passing for molecular graphs
Johannes Gasteiger, Janek Groß, and Stephan Günnemann · 2020
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Panna: Properties from artificial neural network architectures
Ruggero Lot, Franco Pellegrini, Yusuf Shaidu, and Emine Küçükbenli · 2020
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Jax md: a framework for differentiable physics
Samuel Schoenholz and Ekin Dogus Cubuk · 2020
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Towards universal neural network potential for material discovery applicable to arbitrary combination of 45 elements
So Takamoto, Chikashi Shinagawa, Daisuke Motoki, Kosuke Nakago, Wenwen Li, Iori Kurata, Taku Watanabe, Yoshihiro Yayama, Hiroki Iriguchi, Yusuke Asano, et al · 2022
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E (3)-equivariant graph neural networks for data-efficient and accurate interatomic potentials
Simon Batzner, Albert Musaelian, Lixin Sun, Mario Geiger, Jonathan P Mailoa, Mordechai Kornbluth, Nicola Molinari, Tess E Smidt, and Boris Kozinsky · 2022
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Learning local equivariant representations for large-scale atomistic dynamics
Albert Musaelian, Simon Batzner, Anders Johansson, Lixin Sun, Cameron J Owen, Mordechai Kornbluth, and Boris Kozinsky · 2022
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A universal graph deep learning interatomic potential for the periodic table
Chi Chen and Shyue Ping Ong · 2022
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Torchmd-net: equivariant transformers for neural network based molecular potentials
Philipp Thölke and Gianni De Fabritiis · 2022
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e3nn: Euclidean neural networks
Mario Geiger and Tess Smidt · 2022
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Chgnet as a pretrained universal neural network potential for charge-informed atomistic modelling
Bowen Deng, Peichen Zhong, KyuJung Jun, Janosh Riebesell, Kevin Han, Christopher J Bartel, and Gerbrand Ceder · 2023
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Forces are not enough: Benchmark and critical evaluation for machine learning force fields with molecular simulations
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Allegro-legato: Scalable, fast, and robust neural-network quantum molecular dynamics via sharpness-aware minimization
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Scalable hybrid deep neural networks/polarizable potentials biomolecular simulations including long-range effects
Théo Jaffrelot Inizan, Thomas Plé, Olivier Adjoua, Pengyu Ren, Hatice Gökcan, Olexandr Isayev, Louis Lagardère, and Jean-Philip Piquemal · 2023
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Scaling the leading accuracy of deep equivariant models to biomolecular simulations of realistic size, 2023
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Adding conditional control to text-to-image diffusion models
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Gromacs 2023.2 manual, July 2023
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