Graph Fourier Neural ODEs: Modeling Spatial-temporal Multi-scales in Molecular Dynamics

TL;DR

This paper introduces GF-NODE, a novel model combining graph Fourier transforms and Neural ODEs to better capture multi-scale spatial-temporal dynamics in molecular simulations, leading to improved long-term accuracy.

Contribution

The paper proposes GF-NODE, integrating spectral decomposition with continuous-time evolution to enhance molecular dynamics prediction across multiple scales.

Findings

Achieves state-of-the-art accuracy on MD benchmarks.

Effectively captures long-range correlations and local fluctuations.

Demonstrates theoretical link between spectral properties and temporal dynamics.

Abstract

Accurately predicting long-horizon molecular dynamics (MD) trajectories remains a significant challenge, as existing deep learning methods often struggle to retain fidelity over extended simulations. We hypothesize that one key factor limiting accuracy is the difficulty of capturing interactions that span distinct spatial and temporal scales, ranging from high-frequency local vibrations to low-frequency global conformational changes. To address these limitations, we propose Graph Fourier Neural ODEs (GF-NODE), integrating a graph Fourier transform for spatial frequency decomposition with a Neural ODE framework for continuous-time evolution. Specifically, GF-NODE first decomposes molecular configurations into multiple spatial frequency modes using the graph Laplacian, then evolves the frequency components in time via a learnable Neural ODE module that captures both local and global dynamics, and finally reconstructs the updated molecular geometry through an inverse graph Fourier transform. By explicitly modeling high- and low-frequency phenomena in this unified pipeline, GF-NODE captures long-range correlations and local fluctuations more effectively. We provide theoretical insight through heat equation analysis on a simplified diffusion model, demonstrating how graph Laplacian eigenvalues can determine temporal dynamics scales, and crucially validate this correspondence through comprehensive empirical analysis on real molecular dynamics trajectories showing quantitative spatial-temporal correlations across diverse molecular systems. Experimental results on challenging MD benchmarks demonstrate that GF-NODE achieves state-of-the-art accuracy while preserving essential geometrical features over extended simulations. These findings highlight the promise of bridging spectral decomposition with continuous-time modeling to improve the robustness and predictive power of MD simulations.