MDDM: A Molecular Dynamics Diffusion Model to Predict Particle Self-Assembly
This work addresses a domain-specific problem in materials science by providing a faster alternative to traditional molecular dynamics simulations.
The paper tackles the computational expense of molecular simulations by proposing MDDM, a diffusion model that predicts particle self-assembly from input potentials, achieving significant performance improvements over baseline models.
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.