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Coarse-graining (CG) of molecular simulations simplifies the particle representation by grouping selected atoms into pseudo-beads and drastically accelerates simulation.
The Conceptual Foundations of the Statistical Approach in Mechanics
Ehrenfest, P. and Ehrenfest, T · 1912
Earlier work this paper cites.
Statistical mechanics of fluid mixtures
Kirkwood, J. G · 1935
Earlier work this paper cites.
Stereochemistry of polypeptide chain configurations
Ramachandran, G., Ramakrishnan, C., and Sasisekharan, V · 1963
Earlier work this paper cites.
Scaling laws for ising models near t c t_{c}
Kadanoff, L. P · 1966
Earlier work this paper cites.
Renormalization group and critical phenomena. i. renormalization group and the kadanoff scaling picture
Wilson, K. G · 1971
Earlier work this paper cites.
Computer simulation of protein folding
Levitt, M. and Warshel, A · 1975
Earlier work this paper cites.
Crossover from rouse to reptation dynamics: A molecular-dynamics simulation
Kremer, K., Grest, G. S., and Carmesin, I · 1988
Earlier work this paper cites.
Particle mesh ewald: An n log (n) method for ewald sums in large systems
Darden, T., York, D., and Pedersen, L · 1993
Earlier work this paper cites.
Nonequilibrium statistical mechanics
Zwanzig, R · 2001
Earlier work this paper cites.
Escaping free-energy minima
Laio, A. and Parrinello, M · 2002
Earlier work this paper cites.
The martini force field: coarse grained model for biomolecular simulations
Marrink, S. J., Risselada, H. J., Yefimov, S., Tieleman, D. P., and De Vries, A. H · 2007
Earlier work this paper cites.
Disassembly of nanodiscs with cholate
Shih, A. Y., Freddolino, P. L., Sligar, S. G., and Schulten, K · 2007
Earlier work this paper cites.
Is alanine dipeptide a good model for representing the torsional preferences of protein backbones?
Feig, M · 2008
Earlier work this paper cites.
The multiscale coarse-graining method. i. a rigorous bridge between atomistic and coarse-grained models
Noid, W., Chu, J.-W., Ayton, G. S., Krishna, V., Izvekov, S., Voth, G. A., Das, A., and Andersen, H. C · 2008
Earlier work this paper cites.
Reconstruction of atomistic details from coarse-grained structures
Rzepiela, A. J., Schäfer, L. V., Goga, N., Risselada, H. J., De Vries, A. H., and Marrink, S. J · 2010
Earlier work this paper cites.
Multiscale molecular dynamics simulations of micelles: coarse-grain for self-assembly and atomic resolution for finer details
Brocos, P., Mendoza-Espinosa, P., Castillo, R., Mas-Oliva, J., and Pineiro, Á · 2012
Earlier work this paper cites.
Minimizing memory as an objective for coarse-graining
Guttenberg, N., Dama, J. F., Saunders, M. G., Voth, G. A., Weare, J., and Dinner, A. R · 2013
Earlier work this paper cites.
Auto-encoding variational bayes
Kingma, D. P. and Welling, M · 2013
Earlier work this paper cites.
Rdkit: A software suite for cheminformatics, computational chemistry, and predictive modeling, 2013
Landrum, G · 2013
Earlier work this paper cites.
Identification of slow molecular order parameters for markov model construction
Pérez-Hernández, G., Paul, F., Giorgino, T., De Fabritiis, G., and Noé, F · 2013
Earlier work this paper cites.
Adam: A method for stochastic optimization
Kingma, D. P. and Ba, J · 2014
Earlier work this paper cites.
Going backward: a flexible geometric approach to reverse transformation from coarse grained to atomistic models
Wassenaar, T. A., Pluhackova, K., Bockmann, R. A., Marrink, S. J., and Tieleman, D. P · 2014
Earlier work this paper cites.
Automated parametrization of the coarse-grained martini force field for small organic molecules
Bereau, T. and Kremer, K · 2015
Earlier work this paper cites.
Mdtraj: A modern open library for the analysis of molecular dynamics trajectories
McGibbon, R. T., Beauchamp, K. A., Harrigan, M. P., Klein, C., Swails, J. M., Hernández, C. X., Schwantes, C. R., Wang, L.-P., Lane, T. J., and Pande, V. S · 2015
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PyEMMA 2: A Software Package for Estimation, Validation, and Analysis of Markov Models
Scherer, M. K., Trendelkamp-Schroer, B., Paul, F., Pérez-Hernández, G., Hoffmann, M., Plattner, N., Wehmeyer, C., Prinz, J.-H., and Noé, F · 2015
Cited alongside, same era.
Learning structured output representation using deep conditional generative models
Sohn, K., Lee, H., and Yan, X · 2015
Cited alongside, same era.
Categorical reparameterization with gumbel-softmax
Jang, E., Gu, S., and Poole, B · 2016
Cited alongside, same era.
Neural message passing for quantum chemistry
Gilmer, J., Schoenholz, S. S., Riley, P. F., Vinyals, O., and Dahl, G. E · 2017
Cited alongside, same era.
Deep scale-spaces: Equivariance over scale
Worrall, D. and Welling, M · 2019
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Machine learning approach for accurate backmapping of coarse-grained models to all-atom models
An, Y. and Deshmukh, S. A · 2020
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Molecular machine learning with conformer ensembles
Axelrod, S. and Gomez-Bombarelli, R · 2020
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Graph coarsening with neural networks
Cai, C., Wang, D., and Wang, Y · 2020
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Finzi, M., Stanton, S., Izmailov, P., and Wilson, A. G · 2020
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Higgins, I., Matthey, L., Pal, A., Burgess, C. P., Glorot, X., Botvinick, M. M., Mohamed, S., and Lerchner, A · 2017
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Pointnet: Deep learning on point sets for 3d classification and segmentation
Qi, C. R., Su, H., Mo, K., and Guibas, L. J · 2017
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Zaheer, M., Kottur, S., Ravanbakhsh, S., Poczos, B., Salakhutdinov, R., and Smola, A · 2017
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Invariant and equivariant graph networks
Maron, H., Ben-Hamu, H., Shamir, N., and Lipman, Y · 2018
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Euclidean neural networks: e3nn, 2020
Geiger, M., Smidt, T., M., A., Miller, B. K., Boomsma, W., Dice, B., Lapchevskyi, K., Weiler, M., Tyszkiewicz, M., Batzner, S., Uhrin, M., Frellsen, J., Jung, N., Sanborn, S., Rackers, J., and Bailey, M · 2020
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Coarse graining molecular dynamics with graph neural networks
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Learning from protein structure with geometric vector perceptrons
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Directional message passing for molecular graphs
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On learning sets of symmetric elements
Maron, H., Litany, O., Chechik, G., and Fetaya, E · 2020
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Adversarial reverse mapping of equilibrated condensed-phase molecular structures
Stieffenhofer, M., Wand, M., and Bereau, T · 2020
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Se (3)-equivariant graph neural networks for data-efficient and accurate interatomic potentials
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Equivariant subgraph aggregation networks
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Cai, C., Vlassis, N., Magee, L., Ma, R., Xiong, Z., Bahmani, B., Wong, T.-F., Wang, Y., and Sun, W · 2021
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E (n) equivariant graph neural networks
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Equivariant message passing for the prediction of tensorial properties and molecular spectra
Schütt, K. T., Unke, O. T., and Gastegger, M · 2021
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Learning gradient fields for molecular conformation generation
Shi, C., Luo, S., Xu, M., and Tang, J · 2021
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Martini 3: a general purpose force field for coarse-grained molecular dynamics
Souza, P. C., Alessandri, R., Barnoud, J., Thallmair, S., Faustino, I., Grünewald, F., Patmanidis, I., Abdizadeh, H., Bruininks, B. M., Wassenaar, T. A., et al · 2021
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Se (3)-equivariant prediction of molecular wavefunctions and electronic densities
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Learning neural generative dynamics for molecular conformation generation
Xu*, M., Luo*, S., Bengio, Y., Peng, J., and Tang, J · 2021
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An end-to-end framework for molecular conformation generation via bilevel programming
Xu, M., Wang, W., Luo, S., Shi, C., Bengio, Y., Gomez-Bombarelli, R., and Tang, J · 2021
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