mlsanityData to 14 Oct 2026

Implicit Transfer Operator Learning: Multiple Time-Resolution Surrogates for Molecular Dynamics

arXiv 2305.18046 ↗First posted 29 May 2023physics.chem-ph stat.MLcited by 14

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Abstract (arXiv)

Computing properties of molecular systems rely on estimating expectations of the (unnormalized) Boltzmann distribution. Molecular dynamics (MD) is a broadly adopted technique to approximate such quantities. However, stable simulations rely on very small integration time-steps (10-15,s), whereas convergence of some moments, e.g. binding free energy or rates, might rely on sampling processes on time-scales as long as 10-1, s, and these simulations must be repeated for every molecular system independently. Here, we present Implict Transfer Operator (ITO) Learning, a framework to learn surrogates of the simulation process with multiple time-resolutions. We implement ITO with denoising diffusion probabilistic models with a new SE(3) equivariant architecture and show the resulting models can generate self-consistent stochastic dynamics across multiple time-scales, even when the system is only partially observed. Finally, we present a coarse-grained CG-SE3-ITO model which can quantitatively model all-atom molecular dynamics using only coarse molecular representations. As such, ITO provides an important step towards multiple time- and space-resolution acceleration of MD. Code is available at hrefhttps://github.com/olsson-group/itohttps://github.com/olsson-group/ito.

Key facts. Cited by 14 papers in the map (data through 14 Oct 2026). Builds on 30 of its 77 references that are in the map. First posted on arXiv 29 May 2023.

Builds on

30 of 77 references.

References · 30

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18 more
  1. 2021Learning Gradient Fields for Molecular Conformation Generationcited by 163
  2. 2017VAMPnets: Deep learning of molecular kineticscited by 136
  3. 2019Molecular geometry prediction using a deep generative graph neural networkcited by 91
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Citing papers per year: 2023: 2; 2024: 4; 2025: 5; 2026: 3.

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  1. 2024Equivariant Graph Neural Operator for Modeling 3D Dynamicscited by 38
  2. 2024A Survey of Geometric Graph Neural Networks: Data Structures, Models and Applicationscited by 28
  3. 2023Score dynamics: scaling molecular dynamics with picoseconds timestep via conditional diffusion modelcited by 16
  4. 2024EquiJump: Protein Dynamics Simulation via SO(3)-Equivariant Stochastic Interpolantscited by 16
  5. 2026Enhanced Diffusion Sampling: Efficient Rare Event Sampling and Free Energy Calculation with Diffusion Modelscited by 13
  6. 2023Latent Representation and Simulation of Markov Processes via Time-Lagged Information Bottleneckcited by 6
  7. 2025Accelerating Protein Molecular Dynamics Simulation with DeepJumpcited by 6
  8. 2025MarS-FM: Generative Modeling of Molecular Dynamics via Markov State Modelscited by 6
  9. 2025HollowFlow: Efficient Sample Likelihood Evaluation using Hollow Message Passingcited by 5
  10. 2024JAMUN: Bridging Smoothed Molecular Dynamics and Score-Based Learning for Conformational Ensemblescited by 4
  11. 2026Boltzmann-Expected Molecular Design with Decoupled Annealing Flowscited by 2
  12. 2025ATOM: A Pretrained Neural Operator for Multitask Molecular Dynamicscited by 1
2 more
  1. 2025Learning Biomolecular Motion: The Physics-Informed Machine Learning Paradigmcited by 0
  2. 2026Learning Manifold and Ito Dynamics with Branched Neural Rough Differential Equationscited by 0

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  1. 2025Diffusion Models are Molecular Dynamics Simulators
  2. 2025Consistent Sampling and Simulation: Molecular Dynamics with Energy-Based Diffusion Models
  3. 2026Langevin-Informed Transfer Learning: Replacing Target Samples by Black-Box Feedback
  4. 2026Unsupervised Thermodynamics of Molecular Diffusion Models: Action-Operator Semantics and Auditable Free-Energy Readout
  5. 2026Machine learning kinetics from molecular dynamics data
  6. 2026Adaptive recurrent flow map operator learning for reaction diffusion dynamics
  7. 2024Molecular relaxation by reverse diffusion with time step prediction
  8. 2022DiffMD: A Geometric Diffusion Model for Molecular Dynamics Simulations