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 Tools annual

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 Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

We demonstrate how the Gaussian process regression approach can be used toefficiently reconstruct free energy surfaces from umbrella samplingsimulations. By making a prior assumption of smoothness and taking account ofthe sampling noise in a consistent fashion, we achieve a significantimprovement in accuracy over the state of the art in two or more dimensions or,equivalently, a significant cost reduction to obtain the free energy surfacewithin a prescribed tolerance in both regimes of spatially sparse data andshort sampling trajectories. Stemming from its Bayesian interpretation themethod provides meaningful error bars without significant additionalcomputation. A software implementation is made available on www.libatoms.org.

Free energy surface reconstruction from umbrella samples using Gaussian process regression