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

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

29 May 2023arXiv:2305.18046archive 2025-07-28

Mathias Schreiner, Ole Winther, Simon Olsson

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 \href{https://github.com/olsson-group/ito}{https://github.com/olsson-group/ito}.

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