Fast adaptive flat-histogram ensemble for calculating density of states and enhanced sampling in large systems

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We 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 {beta}{sub s}(U) {equivalent_to}{partial_derivative}S(U)/frac/{partial_derivative}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 ({beta}{sub i}, U{sub i}) data points to follow the real change rate of {beta}{sub s}(U) in different U regions and in different systems. Combined with a U-compression transformation, FAFE ... continued below

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Jiang, Yi & Zhou, Xin January 1, 2009.

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We 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 {beta}{sub s}(U) {equivalent_to}{partial_derivative}S(U)/frac/{partial_derivative}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 ({beta}{sub i}, U{sub i}) data points to follow the real change rate of {beta}{sub 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{sup 3/2}) in WLL to O(N{sup 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.

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  • Journal Name: Physical Review Letters

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  • Report No.: LA-UR-09-00387
  • Report No.: LA-UR-09-387
  • Grant Number: AC52-06NA25396
  • Office of Scientific & Technical Information Report Number: 956542
  • Archival Resource Key: ark:/67531/metadc929550

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  • January 1, 2009

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  • Nov. 13, 2016, 7:26 p.m.

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  • Dec. 12, 2016, 3:50 p.m.

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Jiang, Yi & Zhou, Xin. Fast adaptive flat-histogram ensemble for calculating density of states and enhanced sampling in large systems, article, January 1, 2009; [New Mexico]. (digital.library.unt.edu/ark:/67531/metadc929550/: accessed December 13, 2018), University of North Texas Libraries, Digital Library, digital.library.unt.edu; crediting UNT Libraries Government Documents Department.