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Molecular dynamics (MD) simulation predicts the trajectory of atoms by solving Newton's equation of motion with a numeric integrator.
On the determination of molecular fields.—i. from the variation of the viscosity of a gas with temperature
John Edward Jones · 1924
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Computer" experiments" on classical fluids. i. thermodynamical properties of lennard-jones molecules
Loup Verlet · 1967
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A computer simulation method for the calculation of equilibrium constants for the formation of physical clusters of molecules: Application to small water clusters
William C Swope, Hans C Andersen, Peter H Berens, and Kent R Wilson · 1982
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Kinetic moments method for the canonical ensemble distribution
William G Hoover and Brad Lee Holian · 1996
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Understanding molecular simulation: from algorithms to applications , volume 1
Daan Frenkel and Berend Smit · 2001
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Rectified linear units improve restricted boltzmann machines
Vinod Nair and Geoffrey E Hinton · 2010
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Fast analysis of molecular dynamics trajectories with graphics processing units—radial distribution function histogramming
Benjamin G Levine, John E Stone, and Axel Kohlmeyer · 2011
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Lithium ion transport mechanism in ternary polymer electrolyte-ionic liquid mixtures: A molecular dynamics simulation study
Diddo Diddens and Andreas Heuer · 2013
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Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2014
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TensorFlow: Large-scale machine learning on heterogeneous systems, 2015
Martín Abadi, Ashish Agarwal, Paul Barham, Eugene Brevdo, Zhifeng Chen, Craig Citro, Greg S. Corrado, Andy Davis, Jeffrey Dean, Matthieu Devin, Sanjay Ghemawat, Ian Goodfellow, Andrew Harp, Geoffrey Irving, Michael Isard, Yangqing Jia, Rafal Jozefowicz, Lukasz Kaiser, Manjunath Kudlur, Josh Levenberg, Dandelion Mané, Rajat Monga, Sherry Moore, Derek Murray, Chris Olah, Mike Schuster, Jonathon Shlens, Benoit Steiner, Ilya Sutskever, Kunal Talwar, Paul Tucker, Vincent Vanhoucke, Vijay Vasudevan, Fernanda Viégas, Oriol Vinyals, Pete Warden, Martin Wattenberg, Martin Wicke, Yuan Yu, and Xiaoqiang Zheng · 2015
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Thermal conductivity and viscosity of nanofluids: a review of recent molecular dynamics studies
Fatemeh Jabbari, Ali Rajabpour, and Seifollah Saedodin · 2017
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Searching for activation functions
Prajit Ramachandran, Barret Zoph, and Quoc V Le · 2017
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Ion-pairing and electrical conductivity in the ionic liquid 1-n-butyl-3-methylimidazolium methylsulfate [bmim][meso4]: molecular dynamics simulation study
Azam Afandak and Hossein Eslami · 2017
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Molecular dynamics simulations and novel drug discovery
Xuewei Liu, Danfeng Shi, Shuangyan Zhou, Hongli Liu, Huanxiang Liu, and Xiaojun Yao · 2018
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Hamiltonian neural networks
Samuel Greydanus, Misko Dzamba, and Jason Yosinski · 2019
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Deep graph library: A graph-centric, highly-performant package for graph neural networks
Minjie Wang, Da Zheng, Zihao Ye, Quan Gan, Mufei Li, Xiang Song, Jinjing Zhou, Chao Ma, Lingfan Yu, Yu Gai, Tianjun Xiao, Tong He, George Karypis, Jinyang Li, and Zheng Zhang · 2019
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Learning molecular dynamics with simple language model built upon long short-term memory neural network
Sun-Ting Tsai, En-Jui Kuo, and Pratyush Tiwary · 2020
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Simulating molecular dynamics with large timesteps using recurrent neural networks
J Kadupitiya, Geoffrey C Fox, and Vikram Jadhao · 2020
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Mind reading of the proteins: Deep-learning to forecast molecular dynamics
Chitrak Gupta, John Kevin Cava, Daipayan Sarkar, Eric A Wilson, John Vant, Steven Murray, Abhishek Singharoy, and Shubhra Kanti Karmaker · 2020
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Structure-preserving method for reconstructing unknown hamiltonian systems from trajectory data
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Zhengdao Chen, Jianyu Zhang, Martin Arjovsky, and Léon Bottou · 2019
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Deep lagrangian networks: Using physics as model prior for deep learning
Michael Lutter, Christian Ritter, and Jan Peters · 2019
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Hamiltonian generative networks
Peter Toth, Danilo Jimenez Rezende, Andrew Jaegle, Sébastien Racanière, Aleksandar Botev, and Irina Higgins · 2019
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Symplectic ode-net: Learning hamiltonian dynamics with control
Yaofeng Desmond Zhong, Biswadip Dey, and Amit Chakraborty · 2019
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Machine learning enables long time scale molecular photodynamics simulations
Julia Westermayr, Michael Gastegger, Maximilian FSJ Menger, Sebastian Mai, Leticia González, and Philipp Marquetand · 2019
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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 E Weinan
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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 A Saidi, Roberto Car, et al
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Kailiang Wu, Tong Qin, and Dongbin Xiu · 2020
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Lithium ion conduction in cathode coating materials from on-the-fly machine learning
Chuhong Wang, Koutarou Aoyagi, Pandu Wisesa, and Tim Mueller · 2020
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Accelerating covid-19 research using molecular dynamics simulation
Aditya K Padhi, Soumya Lipsa Rath, and Timir Tripathi · 2021
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Data-driven prediction of general hamiltonian dynamics via learning exactly-symplectic maps
Renyi Chen and Molei Tao · 2021
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E (n) equivariant graph neural networks
Victor Garcia Satorras, Emiel Hoogeboom, and Max Welling · 2021
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