Vernizzi Graziano, Nguyen Trung Dac, Orland Henri, Olvera de la Cruz Monica
Department of Physics and Astronomy, Siena College, Loudonville, New York 12211, USA.
Department of Chemical and Biological Engineering, Northwestern University, Evanston, Illinois 60208, USA.
Phys Rev E. 2020 Feb;101(2-1):021301. doi: 10.1103/PhysRevE.101.021301.
We present an ensemble Monte Carlo growth method to sample the equilibrium thermodynamic properties of random chains. The method is based on the multicanonical technique of computing the density of states in the energy space. Such a quantity is temperature independent, and therefore microcanonical and canonical thermodynamic quantities, including the free energy, entropy, and thermal averages, can be obtained by reweighting with a Boltzmann factor. The algorithm we present combines two approaches: The first is the Monte Carlo ensemble growth method, where a "population" of samples in the state space is considered, as opposed to traditional sampling by long random walks, or iterative single-chain growth. The second is the flat-histogram Monte Carlo, similar to the popular Wang-Landau sampling, or to multicanonical chain-growth sampling. We discuss the performance and relative simplicity of the proposed algorithm, and we apply it to known test cases.
我们提出一种系综蒙特卡罗增长方法，用于对随机链的平衡热力学性质进行采样。该方法基于在能量空间中计算态密度的多正则技术。这样一个量与温度无关，因此包括自由能、熵和热平均值在内的微正则和正则热力学量，可以通过用玻尔兹曼因子重新加权来获得。我们提出的算法结合了两种方法：第一种是蒙特卡罗系综增长方法，其中考虑状态空间中的一组“样本群”，这与通过长随机游走或迭代单链增长进行的传统采样相反。第二种是平直方图蒙特卡罗方法，类似于流行的王-兰道采样或多正则链增长采样。我们讨论了所提出算法的性能和相对简单性，并将其应用于已知的测试案例。
