Loeffler Troy D, Sepehri Aliasghar, Chen Bin
Department of Chemistry, Louisiana State University , Baton Rouge, Louisiana 70803, United States.
J Chem Theory Comput. 2015 Sep 8;11(9):4023-32. doi: 10.1021/acs.jctc.5b00466. Epub 2015 Aug 11.
Reformulation of existing Monte Carlo algorithms used in the study of grand canonical systems has yielded massive improvements in efficiency. Here we present an energy biasing scheme designed to address targeting issues encountered in particle swap moves using sophisticated algorithms such as the Aggregation-Volume-Bias and Unbonding-Bonding methods. Specifically, this energy biasing scheme allows a particle to be inserted to (or removed from) a region that is more acceptable. As a result, this new method showed a several-fold increase in insertion/removal efficiency in addition to an accelerated rate of convergence for the thermodynamic properties of the system.
对用于巨正则系综研究的现有蒙特卡罗算法进行重新设计，已在效率方面取得了巨大提升。在此，我们提出一种能量偏置方案，旨在解决使用诸如聚集-体积偏置和断键-成键方法等复杂算法进行粒子交换移动时遇到的目标问题。具体而言，这种能量偏置方案允许将一个粒子插入到（或从）一个更合适的区域。结果，这种新方法除了使系统热力学性质的收敛速度加快外，还使插入/移除效率提高了几倍。
