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Found 7 papers, 3 papers with code
TorchMD-Net 2.0: Fast Neural Network Potentials for Molecular Simulations
Achieving a balance between computational speed, prediction accuracy, and universal applicability in molecular simulations has been a persistent challenge.
PlayMolecule Viewer: a toolkit for the visualization of molecules and other data
PlayMolecule Viewer is a web-based data visualization toolkit designed to streamline the exploration of data resulting from structural bioinformatics or computer-aided drug design efforts.
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.
NNP/MM: Accelerating molecular dynamics simulations with machine learning potentials and molecular mechanic
Machine learning potentials have emerged as a means to enhance the accuracy of biomolecular simulations.
TorchMD: A deep learning framework for molecular simulations
Molecular dynamics simulations provide a mechanistic description of molecules by relying on empirical potentials.
A Scalable Molecular Force Field Parameterization Method Based on Density Functional Theory and Quantum-Level Machine Learning
We demonstrate that for the case of torchani-ANI-1x NNP, we can parameterize small molecules in a fraction of time compared with an equivalent parameterization using DFT QM calculations while producing more accurate parameters than FF (GAFF2).
Chemical Physics Biological Physics Computational Physics
Dimensionality reduction methods for molecular simulations
Molecular simulations produce very high-dimensional data-sets with millions of data points.
