# Xtram

Lastly, we introduce a random-swapping simulation protocol that can be used with xTRAM, gaining orders-of-magnitude advantages over simulation protocols that require the constraint of sampling from a global equilibrium. Using four exemplary systems of varying complexity, we demonstrate the improved convergence ranging from a twofold improvement to several orders of magnitude of xTRAM in comparison to a direct counting estimator and MBAR, with respect to the invested simulation effort. Here, we introduce the transition-based reweighting analysis method TRAM --a class of estimators that exploit ideas from Markov modeling and only require the simulation data to be in local equilibrium within subsets of the configuration space. X 4, , DOI: Here, we introduce the transition-based reweighting analysis method TRAM --a class of estimators that exploit ideas from Markov modeling and only require the simulation data to be in local equilibrium within subsets of the configuration space.

Using four exemplary systems of varying complexity, we demonstrate the improved convergence ranging from a twofold improvement to several orders of magnitude of xTRAM in comparison to a direct counting estimator and MBAR, with respect to the invested simulation effort. The equilibrium properties of the thermodynamic state of interest e. Lastly, we introduce a random-swapping simulation protocol that can be used with xTRAM, gaining orders-of-magnitude advantages over simulation protocols that require the constraint of sampling from a global equilibrium. Computing the equilibrium properties of complex systems, such as free energy differences, is often hampered by rare events in the dynamics. Here, we introduce the transition-based reweighting analysis method TRAM --a class of estimators that exploit ideas from Markov modeling and only require the simulation data to be in local equilibrium within subsets of the configuration space. Here, we introduce the transition-based reweighting analysis method TRAM --a class of estimators that exploit ideas from Markov modeling and only require the simulation data to be in local equilibrium within subsets of the configuration space. X 4, , DOI: Enhanced sampling methods may be used in order to speed up sampling by, for example, using high temperatures, as in parallel tempering, or simulating with a biasing potential such as in the case of umbrella sampling. Using four exemplary systems of varying complexity, we demonstrate the improved convergence ranging from a twofold improvement to several orders of magnitude of xTRAM in comparison to a direct counting estimator and MBAR, with respect to the invested simulation effort. Enhanced sampling methods may be used in order to speed up sampling by, for example, using high temperatures, as in parallel tempering, or simulating with a biasing potential such as in the case of umbrella sampling. Lastly, we introduce a random-swapping simulation protocol that can be used with xTRAM, gaining orders-of-magnitude advantages over simulation protocols that require the constraint of sampling from a global equilibrium. X 4, , DOI: Computing the equilibrium properties of complex systems, such as free energy differences, is often hampered by rare events in the dynamics. The equilibrium properties of the thermodynamic state of interest e.

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