Research Paper#Molecular Dynamics, Graph Neural Networks, Surrogate Models🔬 ResearchAnalyzed: Jan 4, 2026 00:02
GNN Surrogate Models for Accelerated Molecular Dynamics
Published:Dec 26, 2025 01:22
•1 min read
•ArXiv
Analysis
This paper introduces a graph neural network (GNN) based surrogate model to accelerate molecular dynamics simulations. It bypasses the computationally expensive force calculations and numerical integration of traditional methods by directly predicting atomic displacements. The model's ability to maintain accuracy and preserve physical signatures, like radial distribution functions and mean squared displacement, is significant. This approach offers a promising and efficient alternative for atomistic simulations, particularly in metallic systems.
Key Takeaways
- •Proposes a GNN-based surrogate model for molecular dynamics.
- •Bypasses explicit force calculations, improving computational efficiency.
- •Demonstrates accuracy and preservation of physical properties in aluminum simulations.
- •Offers a promising alternative to traditional molecular dynamics for accelerated atomistic simulations.
Reference
“The surrogate achieves sub angstrom level accuracy within the training horizon and exhibits stable behavior during short- to mid-horizon temporal extrapolation.”