mlsanityData to 7 Oct 2026

MDDM: A Molecular Dynamics Diffusion Model to Predict Particle Self-Assembly

arXiv 2501.17319 ↗First posted 28 Jan 2025cs.LG physics.comp-phcited by 0

On the map: Diffusion models → Flow matching, time series and science

Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in Flow matching, time series and science, one of 14 directions in Diffusion models. Builds on 14 of its 27 references that are in the map. First posted on arXiv 28 Jan 2025.

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14 of 27 references.

Closest ancestors · 4

Same direction, newest first

  1. 2023SE(3) diffusion model with application to protein backbone generation5 Feb
  2. 2023Two for One: Diffusion Models and Force Fields for Coarse-Grained Molecular Dynamics1 Feb
  3. 2022Flow Matching for Generative Modeling6 Oct
  4. 2022DiffMD: A Geometric Diffusion Model for Molecular Dynamics Simulations19 Apr

Other references · 10

  1. 2020Denoising Diffusion Implicit Modelscited by 9,655
  2. 2021Diffusion Models Beat GANs on Image Synthesiscited by 8,185
  3. 2016PointNet: Deep Learning on Point Sets for 3D Classification and Segmentationcited by 5,223
  4. 2021Improved Denoising Diffusion Probabilistic Modelscited by 3,736
  5. 2018Dynamic Graph CNN for Learning on Point Cloudscited by 2,220
  6. 2021Diffusion Probabilistic Models for 3D Point Cloud Generationcited by 734
  7. 2022Understanding Diffusion Models: A Unified Perspectivecited by 337
  8. 2024Transolver: A Fast Transformer Solver for PDEs on General Geometriescited by 299
  9. 2021Rotation Invariant Graph Neural Networks using Spin Convolutionscited by 52
  10. 2021Graph Neural Networks Accelerated Molecular Dynamicscited by 33

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