English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Computing properties of molecular systems rely on estimating expectations ofthe (unnormalized) Boltzmann distribution. Molecular dynamics (MD) is a broadlyadopted technique to approximate such quantities. However, stable simulationsrely on very small integration time-steps ($10^{-15}\,\mathrm{s}$), whereasconvergence of some moments, e.g. binding free energy or rates, might rely onsampling processes on time-scales as long as $10^{-1}\, \mathrm{s}$, and thesesimulations must be repeated for every molecular system independently. Here, wepresent Implict Transfer Operator (ITO) Learning, a framework to learnsurrogates of the simulation process with multiple time-resolutions. Weimplement ITO with denoising diffusion probabilistic models with a new SE(3)equivariant architecture and show the resulting models can generateself-consistent stochastic dynamics across multiple time-scales, even when thesystem is only partially observed. Finally, we present a coarse-grainedCG-SE3-ITO model which can quantitatively model all-atom molecular dynamicsusing only coarse molecular representations. As such, ITO provides an importantstep towards multiple time- and space-resolution acceleration of MD. Code isavailable at\href{https://github.com/olsson-group/ito}{https://github.com/olsson-group/ito}.

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