mlsanityData to 14 Oct 2026

On the Connection Between Diffusion Models and Molecular Dynamics

arXiv 2504.03187 ↗First posted 4 Apr 2025cs.LGcited by 0

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

Abstract (arXiv)

Neural Network Potentials (NNPs) have emerged as a powerful tool for modelling atomic interactions with high accuracy and computational efficiency. Recently, denoising diffusion models have shown promise in NNPs by training networks to remove noise added to stable configurations, eliminating the need for force data during training. In this work, we explore the connection between noise and forces by providing a new, simplified mathematical derivation of their relationship. We also demonstrate how a denoising model can be implemented using a conventional MD software package interfaced with a standard NNP architecture. We demonstrate the approach by training a diffusion-based NNP to simulate a coarse-grained lithium chloride solution and employ data duplication to enhance model performance.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Flow matching, time series and science, one of 14 directions in Diffusion models. Builds on 11 of its 30 references that are in the map. First posted on arXiv 4 Apr 2025.

Builds on

11 of 30 references.

Closest ancestors · 2

Same direction, newest first

  1. 2023Diffusion probabilistic models enhance variational autoencoder for crystal structure generative modeling4 Aug
  2. 2023Two for One: Diffusion Models and Force Fields for Coarse-Grained Molecular Dynamics1 Feb

Other references · 9

  1. 2020Denoising Diffusion Probabilistic Modelscited by 21,076
  2. 2015Deep Unsupervised Learning using Nonequilibrium Thermodynamicscited by 7,058
  3. 2021E(3)-Equivariant Graph Neural Networks for Data-Efficient and Accurate Interatomic Potentialscited by 708
  4. 2021Crystal Diffusion Variational Autoencoder for Periodic Material Generationcited by 270
  5. 2017Deep Potential Molecular Dynamics: a scalable model with the accuracy of quantum mechanicscited by 214
  6. 2022Pre-training via Denoising for Molecular Property Predictioncited by 124
  7. 2024Orb: A Fast, Scalable Neural Network Potentialcited by 66
  8. 2024Generative Modeling of Molecular Dynamics Trajectoriescited by 51
  9. 2019Fast Neural Network Approach for Direct Covariant Forces Prediction in Complex Multi-Element Extended Systemscited by 12

Built on by

Not cited yet.

Similar, not cited

Close in wording, no citation either way

  1. 2024Molecular relaxation by reverse diffusion with time step prediction
  2. 2025Diffusion Models are Molecular Dynamics Simulators
  3. 2023Investigating the Behavior of Diffusion Models for Accelerating Electronic Structure Calculations
  4. 2022DiffMD: A Geometric Diffusion Model for Molecular Dynamics Simulations
  5. 2024Geometric-Facilitated Denoising Diffusion Model for 3D Molecule Generation
  6. 2024Immiscible Diffusion: Accelerating Diffusion Training with Noise Assignment
  7. 2025Graph Diffusion that can Insert and Delete
  8. 2026Breaking the Bottlenecks: Scalable Diffusion Models for 3D Molecular Generation