Fast Adaptive Flat-histogram Ensemble for Calculating Density of States and Enhanced Sampling in Large Systems

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dc.creatorZhou, Xin
dc.creatorJiang, Yi
dc.date2008-11-12
dc.date.accessioned2026-07-07T10:17:40Z
dc.date.available2026-07-07T10:17:40Z
dc.descriptionWe presented an efficient algorithm, fast adaptive flat-histogram ensemble (FAFE), to estimate the density of states (DOS) and to enhance sampling in large systems. FAFE calculates the means of an arbitrary extensive variable $U$ in generalized ensembles to form points on the curve $β_{s}(U) \equiv \frac{\partial S(U)}{\partial U}$, the derivative of the logarithmic DOS. Unlike the popular Wang-Landau-like (WLL) methods, FAFE satisfies the detailed-balance condition through out the simulation and automatically generates non-uniform $(β_{i}, U_{i})$ data points to follow the real change rate of $β_{s}(U)$ in different $U$ regions and in different systems. Combined with a $U-$compression transformation, FAFE reduces the required simulation steps from $O(N^{3/2})$ in WLL to $O(N^{1/2})$, where $N$ is the system size. We demonstrate the efficiency of FAFE in Lennard-Jones liquids with several $N$ values. More importantly, we show its abilities in finding and identifying different macroscopic states including meta-stable states in phase co-existing regions.
dc.description5 pages, 4 figures
dc.identifierhttps://arxiv.org/abs/0811.1829
dc.identifierhttp://arxiv.org/abs/0811.1829
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/173932
dc.subjectStatistical Mechanics
dc.titleFast Adaptive Flat-histogram Ensemble for Calculating Density of States and Enhanced Sampling in Large Systems
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