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Found 15 papers, 6 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.
Two for One: Diffusion Models and Force Fields for Coarse-Grained Molecular Dynamics
Coarse-grained (CG) molecular dynamics enables the study of biological processes at temporal and spatial scales that would be intractable at an atomistic resolution.
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.
Flow-matching -- efficient coarse-graining of molecular dynamics without forces
Coarse-grained (CG) molecular simulations have become a standard tool to study molecular processes on time- and length-scales inaccessible to all-atom simulations.
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.
TorchMD: A deep learning framework for molecular simulations
Molecular dynamics simulations provide a mechanistic description of molecules by relying on empirical potentials.
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.
Ensemble Learning of Coarse-Grained Molecular Dynamics Force Fields with a Kernel Approach
Using ensemble learning and stratified sampling, we propose a 2-layer training scheme that enables GDML to learn an effective coarse-grained model.
Machine learning for protein folding and dynamics
Many aspects of the study of protein folding and dynamics have been affected by the recent advances in machine learning.
Machine learning for molecular simulation
Machine learning (ML) is transforming all areas of science.
Data-driven approximation of the Koopman generator: Model reduction, system identification, and control
We derive a data-driven method for the approximation of the Koopman generator called gEDMD, which can be regarded as a straightforward extension of EDMD (extended dynamic mode decomposition).
Tensor-based computation of metastable and coherent sets
Recent years have seen rapid advances in the data-driven analysis of dynamical systems based on Koopman operator theory and related approaches.
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.
Sparse learning of stochastic dynamic equations
With the rapid increase of available data for complex systems, there is great interest in the extraction of physically relevant information from massive datasets.
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Denoising
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