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Sampling complex free energy surfaces is one of the main challenges of modern atomistic simulation methods.
Equation of state calculations by fast computing machines
Nicholas Metropolis, Arianna W Rosenbluth, Marshall N Rosenbluth, Augusta H Teller, and Edward Teller · 1953
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Nonphysical sampling distributions in Monte Carlo free-energy estimation: Umbrella sampling
G. M. Torrie and J. P. Valleau · 1977
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Computer simulation of local order in condensed phases of silicon
Frank H. Stillinger and Thomas A. Weber · 1985
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Constant pressure molecular dynamics algorithms
Glenn J. Martyna, Douglas J. Tobias, and Michael L. Klein · 1994
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Escaping Free-Energy Minima
Alessandro Laio and Michele Parrinello · 2002
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GROMACS: Fast, flexible, and free, 2005
David Van Der Spoel, Erik Lindahl, Berk Hess, Gerrit Groenhof, Alan E. Mark, and Herman J.C. Berendsen · 2005
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A tutorial on energy-based learning
Yann LeCun, Sumit Chopra, Raia Hadsell, M Ranzato, and F Huang · 2006
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Comparison of multiple amber force fields and development of improved protein backbone parameters, 2006
Viktor Hornak, Robert Abel, Asim Okur, Bentley Strockbine, Adrian Roitberg, and Carlos Simmerling · 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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Canonical sampling through velocity rescaling
Giovanni Bussi, Davide Donadio, and Michele Parrinello · 2007
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LAMMPS-large-scale atomic/molecular massively parallel simulator
S Plimpton · 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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Gaussian approximation potentials: The accuracy of quantum mechanics, without the electrons
Albert P Bartók, Mike C Payne, Risi Kondor, and Gábor Csányi · 2010
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Efficient backprop
Yann A. LeCun, Léon Bottou, Genevieve B. Orr, and Klaus Robert Müller · 2012
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Metadynamics with adaptive gaussians
Davide Branduardi, Giovanni Bussi, and Michele Parrinello · 2012
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Non-strongly-convex smooth stochastic approximation with convergence rate O(1/n)
Francis Bach and Eric Moulines · 2013
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Variational approach to enhanced sampling and free energy calculations
Omar Valsson and Michele Parrinello · 2014
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Adam: A Method for Stochastic Optimization
Diederik P Kingma and Jimmy Ba · 2014
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PLUMED 2: New feathers for an old bird
Gareth A Tribello, Massimiliano Bonomi, Davide Branduardi, Carlo Camilloni, and Giovanni Bussi · 2014
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Well-tempered variational approach to enhanced sampling
Omar Valsson and Michele Parrinello · 2015
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Variationally Optimized Free-Energy Flooding for Rate Calculation
James McCarty, Omar Valsson, Pratyush Tiwary, and Michele Parrinello · 2015
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Coarse graining from variationally enhanced sampling applied to the Ginzburg–Landau model
Michele Invernizzi, Omar Valsson, and Michele Parrinello · 2017
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Solving the quantum many-body problem with artificial neural networks
Giuseppe Carleo and Matthias Troyer · 2017
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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 · 2018
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Collective variable discovery and enhanced sampling using autoencoders: Innovations in network architecture and error function design
Wei Chen, Aik Rui Tan, and Andrew L Ferguson · 2018
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Predictive Collective Variable Discovery with Deep Bayesian Models
Markus Schöberl, Nicholas Zabaras, and Phaedon-Stelios Koutsourelakis · 2018
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Efficient Sampling of High-Dimensional Free-Energy Landscapes with Parallel Bias Metadynamics
Jim Pfaendtner and Massimiliano Bonomi · 2015
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Perspective: Machine learning potentials for atomistic simulations
Jörg Behler · 2016
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Enhancing Important Fluctuations: Rare Events and Metadynamics from a Conceptual Viewpoint
Omar Valsson, Pratyush Tiwary, and Michele Parrinello · 2016
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Exploration, Sampling, and Reconstruction of Free Energy Surfaces with Gaussian Process Regression
Letif Mones, Noam Bernstein, and Gábor Csányi · 2016
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A variational approach to nucleation simulation
Pablo M. Piaggi, Omar Valsson, and Michele Parrinello · 2016
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Bespoke Bias for Obtaining Free Energy Differences within Variationally Enhanced Sampling
James McCarty, Omar Valsson, and Michele Parrinello · 2016
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Variational encoding of complex dynamics
Carlos X. Hernández, Hannah K Wayment-Steele, Mohammad M Sultan, Brooke E Husic, and Vijay S Pande · 2018
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Path collective variables without paths
Dan Mendels, GiovanniMaria Piccini, and Michele Parrinello · 2018
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Reweighted autoencoded variational Bayes for enhanced sampling (RAVE)
João Marcelo Lamim Ribeiro, Pablo Bravo, Yihang Wang, and Pratyush Tiwary · 2018
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Learning free energy landscapes using artificial neural networks
Hythem Sidky and Jonathan K Whitmer · 2018
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Reinforced dynamics for enhanced sampling in large atomic and molecular systems
Linfeng Zhang, Han Wang, and E. Weinan · 2018
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Silicon liquid structure and crystal nucleation from ab-initio deep Metadynamics
Luigi Bonati and Michele Parrinello · 2018
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Multithermal-Multibaric Molecular Simulations from a Variational Principle
Pablo M Piaggi and Michele Parrinello · 2019
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Making the best of a bad situation: a multiscale approach to free energy calculation
Michele Invernizzi and Michele Parrinello · 2019
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Phase diagrams from single molecular dynamics simulations
Pablo M Piaggi and Michele Parrinello · 2019
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