Graphmd: A Two-Module Diffusion Framework for Smooth and Consistent Molecular Dynamics

Molecular dynamics (MD) simulation is a key method for studying complex molecular systems, and deep learning has been widely used to accelerate it. Recently, diffusion models have shown excellent performance in generating high-quality molecular conformations and modeling complex distributions. However, their discrete noise-adding process limits the ability to capture smooth oscillatory behavior between consecutive frames and also poses challenges in maintaining spatial structural consistency and effectively processing molecular graph features. To address this, we propose a two-module approach: a molecular graph interaction module, enhanced with classical potential functions, and a diffusion module that uses the Discrete Cosine Transform (DCT) to better capture smooth molecular motions. These improvements enable our model to achieve strong performance across experiments.