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• W2953065950 abstract "We present a Monte Carlo algorithm based on the stochastic approximation Monte Carlo (SAMC) algorithm for directly calculating the density of states. The proposed method is stochastic approximation with a dynamic update factor (SAD), which dynamically adjusts the update factor ${ensuremath{gamma}}_{t}$ during the course of the simulation. We test this method on a square-well fluid and a 31-atom Lennard-Jones cluster and compare the convergence behavior of several related Monte Carlo methods. We find that both the SAD and $1/t$-Wang-Landau ($1/t$-WL) methods rapidly converge to the correct density of states without the need for the user to specify an arbitrary tunable parameter ${t}_{0}$ as in the case of SAMC. SAD requires as input the temperature range of interest, in contrast with $1/t$-WL, which requires that the user identify the interesting range of energies. The convergence of the $1/t$-WL method is very sensitive to the energy range chosen for the low-temperature heat capacity of the Lennard-Jones cluster. Thus, SAD is more powerful in the common case in which the range of energies is not known in advance." @default.
• W2953065950 created "2019-06-27" @default.
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• W2953065950 date "2020-01-02" @default.
• W2953065950 modified "2023-09-27" @default.
• W2953065950 title "Stochastic approximation Monte Carlo with a dynamic update factor" @default.
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• W2953065950 doi "https://doi.org/10.1103/physreve.101.013301" @default.
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• W2953065950 hasPublicationYear "2020" @default.
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