Fetching the paper…
Reading the bibliography…
Molecular dynamics (MD) simulations play a crucial role in scientific research.
On the Convergence of Adam and Beyond, April 2019
Sashank J. Reddi, Satyen Kale, and Sanjiv Kumar · 1904
Earlier work this paper cites.
PyTorch: An Imperative Style, High-Performance Deep Learning Library, December 2019
Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, Alban Desmaison, Andreas Köpf, Edward Yang, Zach DeVito, Martin Raison, Alykhan Tejani, Sasank Chilamkurthy, Benoit Steiner, Lu Fang, Junjie Bai, and Soumith Chintala · 1912
Earlier work this paper cites.
Computer simulation of protein folding
Michael Levitt and Arieh Warshel · 1975
Earlier work this paper cites.
Molecular dynamics with coupling to an external bath
H. J. C. Berendsen, J. P. M. Postma, W. F. van Gunsteren, A. DiNola, and J. R. Haak · 1984
Earlier work this paper cites.
Molecular-dynamics study of atomic motions in water
Kahled Toukan and Aneesur Rahman · 1985
Earlier work this paper cites.
The OPLS [optimized potentials for liquid simulations] potential functions for proteins, energy minimizations for crystals of cyclic peptides and crambin
William L. Jorgensen and Julian Tirado-Rives · 1988
Earlier work this paper cites.
Molecular dynamics algorithm for condensed systems with multiple time scales
Mark E. Tuckerman, Glenn J. Martyna, and Bruce J. Berne · 1990
Earlier work this paper cites.
Force fields for silicas and aluminophosphates based on ab initio calculations
B. W. H. van Beest, G. J. Kramer, and R. A. van Santen · 1990
Earlier work this paper cites.
Phase behaviour of metastable water
Peter H. Poole, Francesco Sciortino, Ulrich Essmann, and H. Eugene Stanley · 1992
Earlier work this paper cites.
Reversible multiple time scale molecular dynamics
M. Tuckerman, B. J. Berne, and G. J. Martyna · 1992
Earlier work this paper cites.
Nosé–Hoover chains: The canonical ensemble via continuous dynamics
Glenn J. Martyna, Michael L. Klein, and Mark Tuckerman · 1992
Earlier work this paper cites.
Formation of Glasses from Liquids and Biopolymers
C. A. Angell · 1995
Earlier work this paper cites.
Fast Parallel Algorithms for Short-Range Molecular Dynamics
Steve Plimpton · 1995
Earlier work this paper cites.
Superionic and Metallic States of Water and Ammonia at Giant Planet Conditions
C. Cavazzoni, G. L. Chiarotti, S. Scandolo, E. Tosatti, M. Bernasconi, and M. Parrinello · 1999
Earlier work this paper cites.
Supercooled liquids and the glass transition
Pablo G. Debenedetti and Frank H. Stillinger · 2001
Earlier work this paper cites.
Understanding Molecular Simulation
Daan Frenkel and Berend Smit · 2002
Earlier work this paper cites.
Liquid–liquid phase transition in supercooled silicon
Srikanth Sastry and C. Austen Angell · 2003
Earlier work this paper cites.
Flexible simple point-charge water model with improved liquid-state properties
Yujie Wu, Harald L. Tepper, and Gregory A. Voth · 2006
Earlier work this paper cites.
Generalized Neural-Network Representation of High-Dimensional Potential-Energy Surfaces
Jörg Behler and Michele Parrinello · 2007
Earlier work this paper cites.
Canonical sampling through velocity rescaling
Giovanni Bussi, Davide Donadio, and Michele Parrinello · 2007
Earlier work this paper cites.
Amorphous silica modeled with truncated and screened Coulomb interactions: A molecular dynamics simulation study
Antoine Carré, Ludovic Berthier, Jürgen Horbach, Simona Ispas, and Walter Kob · 2007
Earlier work this paper cites.
Gaussian Approximation Potentials: The Accuracy of Quantum Mechanics, without the Electrons
Albert P. Bartók, Mike C. Payne, Risi Kondor, and Gábor Csányi · 2010
Earlier work this paper cites.
Effect of hydrogen bond cooperativity on the behavior of water
Kevin Stokely, Marco G. Mazza, H. Eugene Stanley, and Giancarlo Franzese · 2010
Earlier work this paper cites.
How Fast-Folding Proteins Fold
Kresten Lindorff-Larsen, Stefano Piana, Ron O. Dror, and David E. Shaw · 2011
Earlier work this paper cites.
Metastable liquid–liquid transition in a molecular model of water
Jeremy C. Palmer, Fausto Martelli, Yang Liu, Roberto Car, Athanassios Z. Panagiotopoulos, and Pablo G. Debenedetti · 2014
Earlier work this paper cites.
The phase diagram of high-pressure superionic ice
Jiming Sun, Bryan K. Clark, Salvatore Torquato, and Roberto Car · 2015
Earlier work this paper cites.
Improved Peptide and Protein Torsional Energetics with the OPLS-AA Force Field
Michael J. Robertson, Julian Tirado-Rives, and William L. Jorgensen · 2015
Earlier work this paper cites.
On angular momentum balance for particle systems with periodic boundary conditions
Vitaly A. Kuzkin · 2015
Earlier work this paper cites.
A molecular dynamics study of the interaction of water with external surface of silicalite-1
Konstantin Smirnov · 2016
Earlier work this paper cites.
Temperature fluctuations in canonical systems: Insights from molecular dynamics simulations
J. Hickman and Y. Mishin · 2016
Cited alongside, same era.
A stable compound of helium and sodium at high pressure
Xiao Dong, Artem R. Oganov, Alexander F. Goncharov, Elissaios Stavrou, Sergey Lobanov, Gabriele Saleh, Guang-Rui Qian, Qiang Zhu, Carlo Gatti, Volker L. Deringer, Richard Dronskowski, Xiang-Feng Zhou, Vitali B. Prakapenka, Zuzana Konôpková, Ivan A. Popov, Alexander I. Boldyrev, and Hui-Tian Wang · 2017
Cited alongside, same era.
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
Cited alongside, same era.
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
Cited alongside, same era.
Quantum Dynamics and Spectroscopy of Ab Initio Liquid Water: The Interplay of Nuclear and Electronic Quantum Effects
Ondrej Marsalek and Thomas E. Markland · 2017
High-fidelity molecular dynamics trajectory reconstruction with bi-directional neural networks
Ludwig Winkler, Klaus-Robert Müller, and Huziel E Sauceda · 2022
Later among the works it cites.
Solving Newton’s equations of motion with large timesteps using recurrent neural networks based operators
J C S Kadupitiya, Geoffrey C Fox, and Vikram Jadhao · 2022
Later among the works it cites.
Geometric and physical quantities improve e(3) equivariant message passing
Johannes Brandstetter, Rob Hesselink, Elise van der Pol, Erik J Bekkers, and Max Welling · 2022
Later among the works it cites.
LEARNED COARSE MODELS FOR EFFICIENT TURBULENCE SIMULATION
Kimberly Stachenfeld, Drummond B Fielding, Dmitrii Kochkov, Miles Cranmer, Tobias Pfaff, Jonathan Godwin, Can Cui, Shirley Ho, Peter Battaglia, and Alvaro Sanchez-Gonzalez · 2022
Later among the works it cites.
e3nn: Euclidean Neural Networks, July 2022
Mario Geiger and Tess Smidt · 2022
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
Deep Learning Scaling is Predictable, Empirically, December 2017
Joel Hestness, Sharan Narang, Newsha Ardalani, Gregory Diamos, Heewoo Jun, Hassan Kianinejad, Md Mostofa Ali Patwary, Yang Yang, and Yanqi Zhou · 2017
Cited alongside, same era.
Neural Message Passing for Quantum Chemistry
Justin Gilmer, Samuel S Schoenholz, Patrick F Riley, Oriol Vinyals, and George E Dahl · 2017
Cited alongside, same era.
Adam: A Method for Stochastic Optimization, January 2017
Diederik P. Kingma and Jimmy Ba · 2017
Cited alongside, same era.
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
Cited alongside, same era.
Nathaniel Thomas, Tess Smidt, Steven Kearnes, Lusann Yang, Li Li, Kai Kohlhoff, and Patrick Riley · 2018
Cited alongside, same era.
3D Steerable CNNs: Learning Rotationally Equivariant Features in Volumetric Data
Maurice Weiler, Mario Geiger, Max Welling, Wouter Boomsma, and Taco S Cohen · 2018
Cited alongside, same era.
Anomalous Effects of Velocity Rescaling Algorithms: The Flying Ice Cube Effect Revisited
Efrem Braun, Seyed Mohamad Moosavi, and Berend Smit · 2018
Cited alongside, same era.
Thirty Milliseconds in the Life of a Supercooled Liquid
Camille Scalliet, Benjamin Guiselin, and Ludovic Berthier · 2022
Later among the works it cites.
LAMMPS - a flexible simulation tool for particle-based materials modeling at the atomic, meso, and continuum scales
Aidan P. Thompson, H. Metin Aktulga, Richard Berger, Dan S. Bolintineanu, W. Michael Brown, Paul S. Crozier, Pieter J. In ’T Veld, Axel Kohlmeyer, Stan G. Moore, Trung Dac Nguyen, Ray Shan, Mark J. Stevens, Julien Tranchida, Christian Trott, and Steven J. Plimpton · 2022
Later among the works it cites.
Medium-density amorphous ice
Alexander Rosu-Finsen, Michael B. Davies, Alfred Amon, Han Wu, Andrea Sella, Angelos Michaelides, and Christoph G. Salzmann · 2023
Later among the works it cites.
Albert Musaelian, Anders Johansson, Simon Batzner, and Boris Kozinsky · 2023
Later among the works it cites.
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 · 2023
Later among the works it cites.
Accurate global machine learning force fields for molecules with hundreds of atoms
Stefan Chmiela, Valentin Vassilev-Galindo, Oliver T. Unke, Adil Kabylda, Huziel E. Sauceda, Alexandre Tkatchenko, and Klaus-Robert Müller · 2023
Later among the works it cites.
Ultra-fast interpretable machine-learning potentials
Stephen R. Xie, Matthias Rupp, and Richard G. Hennig · 2023
Later among the works it cites.
Scaling deep learning for materials discovery
Amil Merchant, Simon Batzner, Samuel S. Schoenholz, Muratahan Aykol, Gowoon Cheon, and Ekin Dogus Cubuk · 2023
Later among the works it cites.
Learning Interatomic Potentials at Multiple Scales, October 2023
Xiang Fu, Albert Musaelian, Anders Johansson, Tommi Jaakkola, and Boris Kozinsky · 2023
Later among the works it cites.
Timewarp: transferable acceleration of molecular dynamics by learning time-coarsened dynamics
Leon Klein, Andrew Y. K. Foong, Tor Erlend Fjelde, Bruno Mlodozeniec, Marc Brockschmidt, Sebastian Nowozin, Frank Noé, and Ryota Tomioka · 2023
Later among the works it cites.
Implicit transfer operator learning: Multiple time-resolution models for molecular dynamics
Mathias Schreiner, Ole Winther, and Simon Olsson · 2023
Later among the works it cites.
Simulate Time-integrated Coarse-grained Molecular Dynamics with Multi-scale Graph Networks
Xiang Fu, Tian Xie, Nathan J. Rebello, Bradley Olsen, and Tommi S. Jaakkola · 2023
Later among the works it cites.
Evaluation of the MACE force field architecture: From medicinal chemistry to materials science
Dávid Péter Kovács, Ilyes Batatia, Eszter Sára Arany, and Gábor Csányi · 2023
Later among the works it cites.
Learning skillful medium-range global weather forecasting
Remi Lam, Alvaro Sanchez-Gonzalez, Matthew Willson, Peter Wirnsberger, Meire Fortunato, Ferran Alet, Suman Ravuri, Timo Ewalds, Zach Eaton-Rosen, Weihua Hu, Alexander Merose, Stephan Hoyer, George Holland, Oriol Vinyals, Jacklynn Stott, Alexander Pritzel, Shakir Mohamed, and Peter Battaglia · 2023
Later among the works it cites.
Electrofreezing of liquid water at ambient conditions
Giuseppe Cassone and Fausto Martelli · 2024
Later among the works it cites.
Microscopic mechanisms of pressure-induced amorphous-amorphous transitions and crystallisation in silicon
Zhao Fan and Hajime Tanaka · 2024
Later among the works it cites.
MatterSim: A Deep Learning Atomistic Model Across Elements, Temperatures and Pressures, May 2024
Han Yang, Chenxi Hu, Yichi Zhou, Xixian Liu, Yu Shi, Jielan Li, Guanzhi Li, Zekun Chen, Shuizhou Chen, Claudio Zeni, Matthew Horton, Robert Pinsler, Andrew Fowler, Daniel Zügner, Tian Xie, Jake Smith, Lixin Sun, Qian Wang, Lingyu Kong, Chang Liu, Hongxia Hao, and Ziheng Lu · 2024
Later among the works it cites.
Generative Modeling of Molecular Dynamics Trajectories, September 2024
Bowen Jing, Hannes Stärk, Tommi Jaakkola, and Bonnie Berger · 2024
Later among the works it cites.
Equivariant Graph Neural Operator for Modeling 3D Dynamics, June 2024
Minkai Xu, Jiaqi Han, Aaron Lou, Jean Kossaifi, Arvind Ramanathan, Kamyar Azizzadenesheli, Jure Leskovec, Stefano Ermon, and Anima Anandkumar · 2024
Later among the works it cites.
Observation of Plastic Ice VII by Quasi-Elastic Neutron Scattering
Maria Rescigno, Alberto Toffano, Umbertoluca Ranieri, Leon Andriambariarijaona, Richard Gaal, Stefan Klotz, Michael Marek Koza, Jacques Ollivier, Fausto Martelli, John Russo, Francesco Sciortino, Jose Teixeira, and Livia Eleonora Bove · 2025
Closest in time.
The design space of E(3)-equivariant atom-centred interatomic potentials
Ilyes Batatia, Simon Batzner, Dávid Péter Kovács, Albert Musaelian, Gregor N. C. Simm, Ralf Drautz, Christoph Ortner, Boris Kozinsky, and Gábor Csányi · 2025
Closest in time.
Introduction to machine learning potentials for atomistic simulations
Fabian L Thiemann, Niamh O’Neill, Venkat Kapil, Angelos Michaelides, and Christoph Schran · 2025
Closest in time.
Advances in modeling complex materials: The rise of neuroevolution potentials
Penghua Ying, Cheng Qian, Rui Zhao, Yanzhou Wang, Ke Xu, Feng Ding, Shunda Chen, and Zheyong Fan · 2025
Closest in time.
Constraints on the location of the liquid–liquid critical point in water
F. Sciortino, Y. Zhai, S. L. Bore, and F. Paesani · 2025
Closest in time.