Kim Jaegil, Keyes Thomas, Straub John E
Department of Chemistry, Boston University, Boston, Massachusetts 02215, USA.
J Chem Phys. 2009 Mar 28;130(12):124112. doi: 10.1063/1.3095422.
The replica exchange statistical temperature Monte Carlo algorithm (RESTMC) is presented, extending the single-replica STMC algorithm [J. Kim, J. E. Straub, and T. Keyes, Phys. Rev. Lett. 97, 050601 (2006)] to alleviate the slow convergence of the conventional temperature replica exchange method (t-REM) with increasing system size. In contrast to the Gibbs-Boltzmann sampling at a specific temperature characteristic of the standard t-REM, RESTMC samples a range of temperatures in each replica and achieves a flat energy sampling employing the generalized sampling weight, which is automatically determined via the dynamic modification of the replica-dependent statistical temperature. Faster weight determination, through the dynamic update of the statistical temperature, and the flat energy sampling, maximizing energy overlaps between neighboring replicas, lead to a considerable acceleration in the convergence of simulations even while employing significantly fewer replicas. The performance of RESTMC is demonstrated and quantitatively compared with that of the conventional t-REM under varying simulation conditions for Lennard-Jones 19, 31, and 55 atomic clusters, exhibiting single- and double-funneled energy landscapes.
提出了复制交换统计温度蒙特卡罗算法（RESTMC），它是对单复制 STMC 算法[J. Kim, J. E. Straub, and T. Keyes, Phys. Rev. Lett. 97, 050601 (2006)]的扩展，以缓解传统温度复制交换方法（t-REM）随着系统规模增大而收敛缓慢的问题。与标准 t-REM 在特定温度下的吉布斯 - 玻尔兹曼采样不同，RESTMC 在每个复制中对一系列温度进行采样，并使用广义采样权重实现平坦能量采样，该权重通过对依赖于复制的统计温度进行动态修改自动确定。通过统计温度的动态更新实现更快的权重确定，以及通过最大化相邻复制之间的能量重叠进行平坦能量采样，即使使用明显更少的复制，也能显著加速模拟的收敛。在 Lennard-Jones 19、31 和 55 原子簇的不同模拟条件下，展示了 RESTMC 的性能，并与传统 t-REM 的性能进行了定量比较，这些原子簇呈现出单漏斗和双漏斗能量景观。
