mlsanityData to 7 Oct 2026

Transferable Generative Models Bridge Femtosecond to Nanosecond Time-Step Molecular Dynamics

arXiv 2510.07589 ↗First posted 8 Oct 2025physics.chem-ph stat.MLcited by 8

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Abstract (arXiv)

Understanding molecular structure, dynamics, and reactivity requires bridging processes that occur across widely separated time scales. Conventional molecular dynamics simulations provide atomistic resolution, but their femtosecond time steps limit access to the slow conformational changes and relaxation processes that govern chemical function. Here, we introduce a deep generative modeling framework that accelerates sampling of molecular dynamics by four orders of magnitude while retaining physical realism. Applied to small organic molecules and peptides, the approach enables quantitative characterization of equilibrium ensembles and dynamical relaxation processes that were previously only accessible by costly brute-force simulation. Importantly, the method generalizes across chemical composition and system size, extrapolating to peptides larger than those used for training, and captures chemically meaningful transitions on extended time scales. By expanding the accessible range of molecular motions without sacrificing atomistic detail, this approach opens new opportunities for probing conformational landscapes, thermodynamics, and kinetics in systems central to chemistry and biophysics.

Key facts. Cited by 8 papers in the map (data through 7 Oct 2026). Builds on 26 of its 83 references that are in the map. First posted on arXiv 8 Oct 2025.

Builds on

26 of 83 references.

References · 26

  1. 2022Flow Matching for Generative Modelingcited by 5,426
  2. 2018Neural Ordinary Differential Equationscited by 3,733
  3. 2020Transformers are RNNs: Fast Autoregressive Transformers with Linear Attentioncited by 1,979
  4. 2023Stochastic Interpolants: A Unifying Framework for Flows and Diffusionscited by 897
  5. 2020SE(3)-Transformers: 3D Roto-Translation Equivariant Attention Networkscited by 602
  6. 2022Torsional Diffusion for Molecular Conformer Generationcited by 262
  7. 2023Equivariant flow matchingcited by 131
  8. 2018Machine Learning of coarse-grained Molecular Dynamics Force Fieldscited by 68
  9. 2023Timewarp: Transferable Acceleration of Molecular Dynamics by Learning Time-Coarsened Dynamicscited by 62
  10. 2017Variational approach for learning Markov processes from time series datacited by 51
  11. 2024Generative Modeling of Molecular Dynamics Trajectoriescited by 48
  12. 2024Transferable Boltzmann Generatorscited by 43
14 more
  1. 2024SemlaFlow -- Efficient 3D Molecular Generation with Latent Attention and Equivariant Flow Matchingcited by 40
  2. 2024The Importance of Being Scalable: Improving the Speed and Accuracy of Neural Network Interatomic Potentials Across Chemical Domainscited by 40
  3. 2022Machine Learning Coarse-Grained Potentials of Protein Thermodynamicscited by 37
  4. 2017Molecular enhanced sampling with autoencoders: On-the-fly collective variable discovery and accelerated free energy landscape explorationcited by 24
  5. 2023Navigating protein landscapes with a machine-learned transferable coarse-grained modelcited by 22
  6. 2025FLOWR: Flow Matching for Structure-Aware De Novo, Interaction- and Fragment-Based Ligand Generationcited by 18
  7. 2024EquiJump: Protein Dynamics Simulation via SO(3)-Equivariant Stochastic Interpolantscited by 15
  8. 2020Molecular Latent Space Simulatorscited by 13
  9. 2025Force-Free Molecular Dynamics Through Autoregressive Equivariant Networkscited by 13
  10. 2024Euclidean Fast Attention -- Machine Learning Global Atomic Representations at Linear Costcited by 12
  11. 2025FlashMD: long-stride, universal prediction of molecular dynamicscited by 11
  12. 2025MD-LLM-1: A Large Language Model for Molecular Dynamicscited by 6
  13. 2025Accelerating Protein Molecular Dynamics Simulation with DeepJumpcited by 6
  14. 2022From latent dynamics to meaningful representationscited by 5

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 2; 2026: 6.

8 papers.

  1. 2025HollowFlow: Efficient Sample Likelihood Evaluation using Hollow Message Passingcited by 5
  2. 2026Learning Hamiltonian Flow Maps: Mean Flow Consistency for Large-Timestep Molecular Dynamicscited by 5
  3. 2025Enhanced Sampling for Efficient Learning of Coarse-Grained Machine Learning Potentialscited by 3
  4. 2026Protein Language Model Embeddings Improve Generalization of Implicit Transfer Operatorscited by 3
  5. 2026Boltzmann-Expected Molecular Design with Decoupled Annealing Flowscited by 1
  6. 2026Coarse-Grained Boltzmann Generatorscited by 0
  7. 2026Simulation-Based Quantum System Inference with Neural Posterior Estimationcited by 0
  8. 2026SupraTITO: Transferable Generative Molecular Dynamics for Supramolecular Systemscited by 0