| Autor: |
Pommerenck, Jordan K., Simpson, Tanner T., Perlin, Michael A., Roundy, David |
| Rok vydání: |
2019 |
| Předmět: |
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| Zdroj: |
Phys. Rev. E 101, 013301 (2020) |
| Druh dokumentu: |
Working Paper |
| DOI: |
10.1103/PhysRevE.101.013301 |
| Popis: |
We present a new 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 $\gamma_t$ during the course of the simulation. We test this method on the square-well fluid and the 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 to $1/t$-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. |
| Databáze: |
arXiv |
| Externí odkaz: |
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