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Neural network (NN) potentials are a natural choice for coarse-grained (CG) models.
On information and sufficiency
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Mapping of explicit atom onto united atom potentials
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Deriving effective mesoscale potentials from atomistic simulations
Reith, D., Pütz, M. & Müller-Plathe, F · 2003
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A point-charge force field for molecular mechanics simulations of proteins based on condensed-phase quantum mechanical calculations
Duan, C., Y.and Wu et al · 2003
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Multiscale coarse graining of liquid-state systems
Izvekov, S. & Voth, G. A · 2005
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A general purpose model for the condensed phases of water: Tip4p/2005
Abascal, J. L. F. & Vega, C · 2005
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The MARTINI force field: Coarse grained model for biomolecular simulations
Marrink, S. J., Risselada, H. J., Yefimov, S., Tieleman, D. P. & De Vries, A. H · 2007
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Generalized neural-network representation of high-dimensional potential-energy surfaces
Behler, J. & Parrinello, M · 2007
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Free energy calculations , vol. 86 (Springer, 2007)
Chipot, C. & Pohorille, A · 2007
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The multiscale coarse-graining method. i. a rigorous bridge between atomistic and coarse-grained models
Noid, W. G. et al · 2008
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The relative entropy is fundamental to multiscale and inverse thermodynamic problems
Shell, M. S · 2008
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The multiscale coarse-graining method. ii. numerical implementation for coarse-grained molecular models
Noid, W. et al · 2008
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Quantum differences between heavy and light water
Soper, A. K. & Benmore, C. J · 2008
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Recent advances in implicit solvent-based methods for biomolecular simulations
Chen, J., Brooks III, C. L. & Khandogin, J · 2008
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Experimental parameterization of an energy function for the simulation of unfolded proteins
Norgaard, A. B., Ferkinghoff-Borg, J. & Lindorff-Larsen, K · 2008
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Generalized yvon-born-green theory for molecular systems
Mullinax, J. & Noid, W · 2009
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Anomalous waterlike behavior in spherically-symmetric water models optimized with the relative entropy
Chaimovich, A. & Shell, M. S · 2009
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Comparative atomistic and coarse-grained study of water: What do we lose by coarse-graining?
Wang, H., Junghans, C. & Kremer, K · 2009
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Versatile object-oriented toolkit for coarse-graining applications
Ruhle, V., Junghans, C., Lukyanov, A., Kremer, K. & Andrienko, D · 2009
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Noise-contrastive estimation: A new estimation principle for unnormalized statistical models
Gutmann, M. & Hyvärinen, A · 2010
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Relative entropy as a universal metric for multiscale errors
Chaimovich, A. & Shell, M. S · 2010
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Atom-centered symmetry functions for constructing high-dimensional neural network potentials
Behler, J · 2011
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Coarse-graining errors and numerical optimization using a relative entropy framework
Chaimovich, A. & Shell, M. S · 2011
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Obtaining fully dynamic coarse-grained models from md
Espanol, P. & Zuniga, I · 2011
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Coarse-graining entropy, forces, and structures
Rudzinski, J. F. & Noid, W · 2011
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Iterative optimization of molecular mechanics force fields from NMR data of full-length proteins
Li, D. W. & Brüschweiler, R · 2011
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Less is more: Sampling chemical space with active learning
Smith, J. S., Nebgen, B., Lubbers, N., Isayev, O. & Roitberg, A. E · 2018
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Recent advances in coarse-grained models for biomolecules and their applications
Singh, N. & Li, W · 2019
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Machine Learning of Coarse-Grained Molecular Dynamics Force Fields
Wang, J. et al · 2019
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Active learning of uniformly accurate interatomic potentials for materials simulation
Zhang, L., Lin, D.-Y., Wang, H., Car, R. & Weinan, E · 2019
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Learning Protein Structure with a Differentiable Simulator
Ingraham, J., Riesselman, A., Sander, C. & Marks, D · 2019
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Directional Message Passing for Molecular Graphs
Klicpera, J., Groß, J. & Günnemann, S · 2020
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Metadynamics
Barducci, A., Bonomi, M. & Parrinello, M · 2011
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A new multiscale algorithm and its application to coarse-grained peptide models for self-assembly
Carmichael, S. P. & Shell, M. S · 2012
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Perspective: Coarse-grained models for biomolecular systems
Noid, W. G · 2013
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Variational optimization of an all-atom implicit solvent force field to match explicit solvent simulation data
Bottaro, S., Lindorff-Larsen, K. & Best, R. B · 2013
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Ensemble simulations with discrete classical dynamics
Toxvaerd, S · 2013
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The power of coarse graining in biomolecular simulations
Ingólfsson, H. I. et al · 2014
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Fast and uncertainty-aware directional message passing for non-equilibrium molecules
Klicpera, J., Giri, S., Margraf, J. T. & Günnemann, S · 2020
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OrbNet: Deep learning for quantum chemistry using symmetry-adapted atomic-orbital features
Qiao, Z., Welborn, M., Anandkumar, A., Manby, F. R. & Miller, T. F · 2020
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Active learning a coarse-grained neural network model for bulk water from sparse training data
Loeffler, T. D., Patra, T. K., Chan, H. & Sankaranarayanan, S. K · 2020
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Coarse Graining Molecular Dynamics with Graph Neural Networks
Husic, B. E. et al · 2020
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On-the-fly active learning of interatomic potentials for large-scale atomistic simulations
Jinnouchi, R., Miwa, K., Karsai, F., Kresse, G. & Asahi, R · 2020
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JAX, M.D.: A Framework for Differentiable Physics
Schoenholz, S. S. & Cubuk, E. D · 2020
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Machine learning for metallurgy III: A neural network potential for Al-Mg-Si
Jain, A. C. P., Marchand, D., Glensk, A., Ceriotti, M. & Curtin, W. A · 2021
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A fourth-generation high-dimensional neural network potential with accurate electrostatics including non-local charge transfer
Ko, T. W., Finkler, J. A., Goedecker, S. & Behler, J · 2021
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Machine learning implicit solvation for molecular dynamics
Chen, Y. et al · 2021
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Learning neural network potentials from experimental data via differentiable trajectory reweighting
Thaler, S. & Zavadlav, J · 2021
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Machine learning in qm/mm molecular dynamics simulations of condensed-phase systems
Böselt, L., Thürlemann, M. & Riniker, S · 2021
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Ultra-fast interpretable machine-learning potentials
Xie, S. R., Rupp, M. & Hennig, R. G · 2021
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Automated discovery of a robust interatomic potential for aluminum
Smith, J. S. et al · 2021
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Designing self-assembling kinetics with differentiable statistical physics models
Goodrich, C. P., King, E. M., Schoenholz, S. S., Cubuk, E. D. & Brenner, M. P · 2021
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Torchmd: A deep learning framework for molecular simulations
Doerr, S. et al · 2021
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E (3)-equivariant graph neural networks for data-efficient and accurate interatomic potentials
Batzner, S. et al · 2022
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Mace: Higher order equivariant message passing neural networks for fast and accurate force fields
Batatia, I., Kovács, D. P., Simm, G. N., Ortner, C. & Csányi, G · 2022
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Contrastive learning of coarse-grained force fields
Ding, X. & Zhang, B · 2022
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Force-matching coarse-graining without forces
Köhler, J., Chen, Y., Krämer, A., Clementi, C. & Noé, F · 2022
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The design space of e (3)-equivariant atom-centered interatomic potentials
Batatia, I. et al · 2022
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How robust are modern graph neural network potentials in long and hot molecular dynamics simulations?
Stocker, S., Gasteiger, J., Becker, F., Günnemann, S. & Margraf, J. T · 2022
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Fu, X. et al · 2022
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