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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.
Implicit Transfer Operator Learning: Multiple Time-Resolution Surrogates for Molecular Dynamics
Computing properties of molecular systems rely on estimating expectations of the (unnormalized) Boltzmann distribution.
Machine Learning in Molecular Dynamics Simulations of Biomolecular Systems
Machine learning (ML) has emerged as a pervasive tool in science, engineering, and beyond.
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.
Boltzmann Generators -- Sampling Equilibrium States of Many-Body Systems with Deep Learning
Computing equilibrium states in condensed-matter many-body systems, such as solvated proteins, is a long-standing challenge.
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.
