mlsanityData to 14 Oct 2026

Generative Quasi-Continuum Modeling of Confined Fluids at the Nanoscale

arXiv 2509.08223 ↗First posted 10 Sep 2025physics.comp-ph cs.LGcited by 0

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Abstract (arXiv)

We present a data-efficient, multiscale framework for predicting the density profiles of confined fluids at the nanoscale. While accurate density estimates require prohibitively long timescales that are inaccessible by ab initio molecular dynamics (AIMD) simulations, machine-learned molecular dynamics (MLMD) offers a scalable alternative, enabling the generation of force predictions at ab initio accuracy with reduced computational cost. However, despite their efficiency, MLMD simulations remain constrained by femtosecond timesteps, which limit their practicality for computing long-time averages needed for accurate density estimation. To address this, we propose a conditional denoising diffusion probabilistic model (DDPM) based quasi-continuum approach that predicts the long-time behavior of force profiles along the confinement direction, conditioned on noisy forces extracted from a limited AIMD dataset. The predicted smooth forces are then linked to continuum theory via the Nernst-Planck equation to reveal the underlying density behavior. We test the framework on water confined between two graphene nanoscale slits and demonstrate that density profiles for channel widths outside of the training domain can be recovered with ab initio accuracy. Compared to AIMD and MLMD simulations, our method achieves orders-of-magnitude speed-up in runtime and requires significantly less training data than prior works.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 13 of its 91 references that are in the map. First posted on arXiv 10 Sep 2025.

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13 of 91 references.

References · 13

  1. 2020Denoising Diffusion Probabilistic Modelscited by 21,076
  2. 2015Deep Unsupervised Learning using Nonequilibrium Thermodynamicscited by 7,058
  3. 2021Improved Denoising Diffusion Probabilistic Modelscited by 3,761
  4. 2021Image Super-Resolution via Iterative Refinementcited by 1,511
  5. 2021E(3)-Equivariant Graph Neural Networks for Data-Efficient and Accurate Interatomic Potentialscited by 708
  6. 2022MACE: Higher Order Equivariant Message Passing Neural Networks for Fast and Accurate Force Fieldscited by 474
  7. 2022Learning Local Equivariant Representations for Large-Scale Atomistic Dynamicscited by 256
  8. 2017Deep Potential Molecular Dynamics: a scalable model with the accuracy of quantum mechanicscited by 214
  9. 2022Forces are not Enough: Benchmark and Critical Evaluation for Machine Learning Force Fields with Molecular Simulationscited by 95
  10. 2021Graph Neural Networks Accelerated Molecular Dynamicscited by 33
  11. 2022DeScoD-ECG: Deep Score-Based Diffusion Model for ECG Baseline Wander and Noise Removalcited by 25
  12. 2022Simulate Time-integrated Coarse-grained Molecular Dynamics with Multi-Scale Graph Networkscited by 23
1 more
  1. 2024Stability-Aware Training of Machine Learning Force Fields with Differentiable Boltzmann Estimatorscited by 8

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  3. 2025On the Connection Between Diffusion Models and Molecular Dynamics
  4. 2026Generative Pseudo-Force Fields for Molecular Generation
  5. 2025Diffusion Models are Molecular Dynamics Simulators
  6. 2026AquaGen: Scaling generative models to molecular dynamics precision on thousands of atoms
  7. 2024A predictive machine learning force field framework for liquid electrolyte development
  8. 2024Flow Matching for Accelerated Simulation of Atomic Transport in Crystalline Materials