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Found 6 papers, 2 papers with code
Navigating protein landscapes with a machine-learned transferable coarse-grained model
The most popular and universally predictive protein simulation models employ all-atom molecular dynamics (MD), but they come at extreme computational cost.
Statistically Optimal Force Aggregation for Coarse-Graining Molecular Dynamics
A widely used methodology for learning CG force-fields maps forces from all-atom molecular dynamics to the CG representation and matches them with a CG force-field on average.
Machine Learning Coarse-Grained Potentials of Protein Thermodynamics
The coarse-grained models are capable of accelerating the dynamics by more than three orders of magnitude while preserving the thermodynamics of the systems.
Machine Learning Implicit Solvation for Molecular Dynamics
Here, we leverage machine learning (ML) and multi-scale coarse graining (CG) in order to learn implicit solvent models that can approximate the energetic and thermodynamic properties of a given explicit solvent model with arbitrary accuracy, given enough training data.
Coarse Graining Molecular Dynamics with Graph Neural Networks
5, 755 (2019)] demonstrated that the existence of such a variational limit enables the use of a supervised machine learning framework to generate a coarse-grained force field, which can then be used for simulation in the coarse-grained space.
Machine Learning of coarse-grained Molecular Dynamics Force Fields
We show that CGnets can capture all-atom explicit-solvent free energy surfaces with models using only a few coarse-grained beads and no solvent, while classical coarse-graining methods fail to capture crucial features of the free energy surface.
