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Molecular dynamics (MD) simulation is essential for various scientific domains but computationally expensive.
Nonphysical sampling distributions in monte carlo free-energy estimation: Umbrella sampling
Glenn M Torrie and John P Valleau · 1977
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Force field for computation of conformational energies, structures, and vibrational frequencies of aromatic polyesters
Huai Sun · 1994
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Long short-term memory
Sepp Hochreiter and Jürgen Schmidhuber · 1997
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Computer simulations of poly (ethylene oxide): force field, pvt diagram and cyclization behaviour
David Rigby, Huai Sun, and BE Eichinger · 1997
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A fast and high quality multilevel scheme for partitioning irregular graphs
George Karypis and Vipin Kumar · 1998
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Replica-exchange molecular dynamics method for protein folding
Yuji Sugita and Yuko Okamoto · 1999
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Escaping free-energy minima
Alessandro Laio and Michele Parrinello · 2002
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Zinc- a free database of commercially available compounds for virtual screening
John J Irwin and Brian K Shoichet · 2005
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Assessing the accuracy of metadynamics
Alessandro Laio, Antonio Rodriguez-Fortea, Francesco Luigi Gervasio, Matteo Ceccarelli, and Michele Parrinello · 2005
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Generalized neural-network representation of high-dimensional potential-energy surfaces
Jörg Behler and Michele Parrinello · 2007
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The martini force field: coarse grained model for biomolecular simulations
Siewert J Marrink, H Jelger Risselada, Serge Yefimov, D Peter Tieleman, and Alex H De Vries · 2007
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Well-tempered metadynamics: a smoothly converging and tunable free-energy method
Alessandro Barducci, Giovanni Bussi, and Michele Parrinello · 2008
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Systematic coarse-graining methods for soft matter simulations–a review
Emiliano Brini, Elena A Algaer, Pritam Ganguly, Chunli Li, Francisco Rodríguez-Ropero, and Nico FA van der Vegt · 2013
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Identification of slow molecular order parameters for markov model construction
Guillermo Pérez-Hernández, Fabian Paul, Toni Giorgino, Gianni De Fabritiis, and Frank Noé · 2013
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Variational approach to enhanced sampling and free energy calculations
Omar Valsson and Michele Parrinello · 2014
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Enhanced sampling techniques in molecular dynamics simulations of biological systems
Rafael C. Bernardi, Marcelo C.R. Melo, and Klaus Schulten · 2015
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Bayesian parametrization of coarse-grain dissipative dynamics models
Alain Dequidt and Jose G Solano Canchaya · 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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Coarse-grained protein models and their applications
Sebastian Kmiecik, Dominik Gront, Michal Kolinski, Lukasz Wieteska, Aleksandra Elzbieta Dawid, and Andrzej Kolinski · 2016
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Commute maps: Separating slowly mixing molecular configurations for kinetic modeling
Frank Noé, Ralf Banisch, and Cecilia Clementi · 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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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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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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Kinetics from replica exchange molecular dynamics simulations
Lukas S Stelzl and Gerhard Hummer · 2017
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Neural ordinary differential equations
Ricky TQ Chen, Yulia Rubanova, Jesse Bettencourt, and David K Duvenaud · 2018
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Towards exact molecular dynamics simulations with machine-learned force fields
Stefan Chmiela, Huziel E Sauceda, Klaus-Robert Müller, and Alexandre Tkatchenko · 2018
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Data-driven model reduction and transfer operator approximation
Stefan Klus, Feliks Nüske, Péter Koltai, Hao Wu, Ioannis Kevrekidis, Christof Schütte, and Frank Noé · 2018
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Vampnets for deep learning of molecular kinetics
Andreas Mardt, Luca Pasquali, Hao Wu, and Frank Noé · 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
Nathaniel Thomas, Tess Smidt, Steven Kearnes, Lusann Yang, Li Li, Kai Kohlhoff, and Patrick Riley · 2018
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Torchmd: A deep learning framework for molecular simulations
Stefan Doerr, Maciej Majewski, Adrià Pérez, Andreas Krämer, Cecilia Clementi, Frank Noe, Toni Giorgino, and Gianni De Fabritiis · 2021
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Machine-learned potentials for next-generation matter simulations
Pascal Friederich, Florian Häse, Jonny Proppe, and Alán Aspuru-Guzik · 2021
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Gemnet: Universal directional graph neural networks for molecules
Johannes Gasteiger, Florian Becker, and Stephan Günnemann · 2021
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Differentiable molecular simulation can learn all the parameters in a coarse-grained force field for proteins
Joe G Greener and David T Jones · 2021
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Physics-aware, probabilistic model order reduction with guaranteed stability
Sebastian Kaltenbach and Phaedon-Stelios Koutsourelakis · 2021
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Deep generative markov state models
Hao Wu, Andreas Mardt, Luca Pasquali, and Frank Noe · 2018
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Development of coarse-grained models for polymers by trajectory matching
Kévin Kempfer, Julien Devemy, Alain Dequidt, Marc Couty, and Patrice Malfreyt · 2019
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Boltzmann generators: Sampling equilibrium states of many-body systems with deep learning
Frank Noé, Simon Olsson, Jonas Köhler, and Hao Wu · 2019
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Generative modeling by estimating gradients of the data distribution
Yang Song and Stefano Ermon · 2019
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Pet-raft and saxs: High throughput tools to study compactness and flexibility of single-chain polymer nanoparticles
Rahul Upadhya, N Sanjeeva Murthy, Cody L Hoop, Shashank Kosuri, Vikas Nanda, Joachim Kohn, Jean Baum, and Adam J Gormley · 2019
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Machine learning of coarse-grained molecular dynamics force fields
Jiang Wang, Simon Olsson, Christoph Wehmeyer, Adrià Pérez, Nicholas E Charron, Gianni De Fabritiis, Frank Noé, and Cecilia Clementi · 2019
Cited alongside, same era.
Coarse-graining auto-encoders for molecular dynamics
Wujie Wang and Rafael Gómez-Bombarelli · 2019
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Introducing memory in coarse-grained molecular simulations
Viktor Klippenstein, Madhusmita Tripathy, Gerhard Jung, Friederike Schmid, and Nico FA van der Vegt · 2021
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Linear atomic cluster expansion force fields for organic molecules: beyond rmse
Dávid Péter Kovács, Cas van der Oord, Jiri Kucera, Alice EA Allen, Daniel J Cole, Christoph Ortner, and Gábor Csányi · 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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E(n) equivariant graph neural networks
Víctor Garcia Satorras, Emiel Hoogeboom, and Max Welling · 2021
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Evaluating coarse-grained martini force-fields for capturing the ripple phase of lipid membranes
Pradyumn Sharma, Rajat Desikan, and K Ganapathy Ayappa · 2021
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Learning gradient fields for molecular conformation generation
Chence Shi, Shitong Luo, Minkai Xu, and Jian Tang · 2021
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Martini 3: a general purpose force field for coarse-grained molecular dynamics
Paulo CT Souza, Riccardo Alessandri, Jonathan Barnoud, Sebastian Thallmair, Ignacio Faustino, Fabian Grünewald, Ilias Patmanidis, Haleh Abdizadeh, Bart MH Bruininks, Tsjerk A Wassenaar, et al · 2021
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Equivariant transformers for neural network based molecular potentials
Philipp Thölke and Gianni De Fabritiis · 2021
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Accelerated simulations of molecular systems through learning of effective dynamics
Pantelis R Vlachas, Julija Zavadlav, Matej Praprotnik, and Petros Koumoutsakos · 2021
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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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Ensuring thermodynamic consistency with invertible coarse-graining
Shriram Chennakesavalu, David J Toomer, and Grant M Rotskoff · 2022
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Xiang Fu, Zhenghao Wu, Wujie Wang, Tian Xie, Sinan Keten, Rafael Gomez-Bombarelli, and Tommi Jaakkola · 2022
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Graph neural networks accelerated molecular dynamics
Zijie Li, Kazem Meidani, Prakarsh Yadav, and Amir Barati Farimani · 2022
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Accurate sampling of macromolecular conformations using adaptive deep learning and coarse-grained representation
Amr H Mahmoud, Matthew Masters, Soo Jung Lee, and Markus A Lill · 2022
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Consistent and transferable force fields for statistical copolymer systems at the mesoscale
RL Nkepsu Mbitou, F Goujon, A Dequidt, B Latour, J Devémy, R Blaak, N Martzel, C Emeriau-Viard, J Tchoufag, S Garruchet, et al · 2022
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How robust are modern graph neural network potentials in long and hot molecular dynamics simulations?
Sina Stocker, Johannes Gasteiger, Florian Becker, Stephan Günnemann, and Johannes T Margraf · 2022
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Towards universal neural network potential for material discovery applicable to arbitrary combination of 45 elements
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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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Accelerating amorphous polymer electrolyte screening by learning to reduce errors in molecular dynamics simulated properties
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Two for one: Diffusion models and force fields for coarse-grained molecular dynamics
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Timewarp: Transferable acceleration of molecular dynamics by learning time-coarsened dynamics
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