The discovery and study of new material systems rely on molecular simulations that often come with significant computational expense. We propose MDDM, a Molecular Dynamics Diffusion Model, which is capable of predicting a valid output conformation for a given input pair potential function. After training MDDM on a large dataset of molecular dynamics self-assembly results, the proposed model can convert uniform noise into a meaningful output particle structure corresponding to an arbitrary input potential. The model's architecture has domain-specific properties built-in, such as satisfying periodic boundaries and being invariant to translation. The model significantly outperforms the baseline point-cloud diffusion model for both unconditional and conditional generation tasks.
@article{arxiv.2501.17319,
title = {MDDM: A Molecular Dynamics Diffusion Model to Predict Particle Self-Assembly},
author = {Kevin Ferguson and Yu-hsuan Chen and Levent Burak Kara},
journal= {arXiv preprint arXiv:2501.17319},
year = {2025}
}