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Found 13 papers, 5 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.
OpenMM 8: Molecular Dynamics Simulation with Machine Learning Potentials
Machine learning plays an important and growing role in molecular simulation.
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.
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.
Smooth Normalizing Flows
In this work, we introduce a class of smooth mixture transformations working on both compact intervals and hypertori.
Lettuce: PyTorch-based Lattice Boltzmann Framework
Lettuce enables GPU accelerated calculations with minimal source code, facilitates rapid prototyping of LBM models, and enables integrating LBM simulations with PyTorch's deep learning and automatic differentiation facility.
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.
Cubature rules for weakly and fully compressible off-lattice Boltzmann methods
Off-lattice Boltzmann methods increase the flexibility and applicability of lattice Boltzmann methods by decoupling the discretizations of time, space, and particle velocities.
Computational Physics Fluid Dynamics
TorchMD: A deep learning framework for molecular simulations
Molecular dynamics simulations provide a mechanistic description of molecules by relying on empirical potentials.
High-order semi-Lagrangian kinetic scheme for compressible turbulence
Turbulent compressible flows are traditionally simulated using explicit time integrators applied to discretized versions of the Navier-Stokes equations.
Computational Physics Statistical Mechanics Fluid Dynamics
Master regulators as order parameters of gene expression states
Cell type-specific gene expression patterns are represented as memory states of a Hopfield neural network model.
Training Invertible Linear Layers through Rank-One Perturbations
Many types of neural network layers rely on matrix properties such as invertibility or orthogonality.
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.
