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 Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Computing the equilibrium properties of complex systems, such as free energydifferences, is often hampered by rare events in the dynamics. Enhancedsampling methods may be used in order to speed up sampling by, for example,using high temperatures, as in parallel tempering, or simulating with a biasingpotential such as in the case of umbrella sampling. The equilibrium propertiesof the thermodynamic state of interest (e.g., lowest temperature or unbiasedpotential) can be computed using reweighting estimators such as the weightedhistogram analysis method or the multistate Bennett acceptance ratio (MBAR).weighted histogram analysis method and MBAR produce unbiased estimates, thesimulation samples from the global equilibria at their respective thermodynamicstate--a requirement that can be prohibitively expensive for some simulationssuch as a large parallel tempering ensemble of an explicitly solvatedbiomolecule. Here, we introduce the transition-based reweighting analysismethod (TRAM)--a class of estimators that exploit ideas from Markov modelingand only require the simulation data to be in local equilibrium within subsetsof the configuration space. We formulate the expanded TRAM (xTRAM) estimatorthat is shown to be asymptotically unbiased and a generalization of MBAR. Usingfour exemplary systems of varying complexity, we demonstrate the improvedconvergence (ranging from a twofold improvement to several orders of magnitude)of xTRAM in comparison to a direct counting estimator and MBAR, with respect tothe invested simulation effort. Lastly, we introduce a random-swappingsimulation protocol that can be used with xTRAM, gaining orders-of-magnitudeadvantages over simulation protocols that require the constraint of samplingfrom a global equilibrium.

xTRAM: Estimating equilibrium expectations from time-correlated simulation data at multiple thermodynamic states