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

• URL: http://arxiv.org/abs/2305.18046v2
• Date: Sat, 28 Oct 2023 09:36:51 GMT
• Title: Implicit Transfer Operator Learning: Multiple Time-Resolution Surrogates for Molecular Dynamics
• Authors: Mathias Schreiner and Ole Winther and Simon Olsson
• Abstract summary: We present Implict Transfer Operator (ITO) Learning, a framework to learn surrogates of the simulation process with multiple time-resolutions. We also present a coarse-grained CG-SE3-ITO model which can quantitatively model all-atom molecular dynamics.
• Score: 8.35780131268962
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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 ($10^{-15}\,\mathrm{s}$), whereas convergence of some moments, e.g. binding free energy or rates, might rely on sampling processes on time-scales as long as $10^{-1}\, \mathrm{s}$, 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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