Generalized ensemble method applied to study systems with strong first order transitions

26th IUPAP Conference on Computational Physics

物理学;计算机科学

Malolepsza, E.^1 ; Kim, J.^2 ; Keyes, T.^1

Department of Chemistry, Boston University, Boston

MA

02215-2521, United States^1

Broad Institute of MIT and Harvard, Cambridge

MA

02142, United States^2

关键词: Energy distributions;    First order transitions;    First-order phase transitions;    Molecular simulations;    Monte carlo algorithms;    Replica exchange method;    Solid/liquid transitions;    Statistical temperature;   

Others  :  https://iopscience.iop.org/article/10.1088/1742-6596/640/1/012003/pdf
DOI  :  10.1088/1742-6596/640/1/012003

学科分类：计算机科学（综合）

来源: IOP

【 摘 要 】

At strong first-order phase transitions, the entropy versus energy or, at constant pressure, enthalpy, exhibits convex behavior, and the statistical temperature curve correspondingly exhibits an S-loop or back-bending. In the canonical and isothermal-isobaric ensembles, with temperature as the control variable, the probability density functions become bimodal with peaks localized outside of the S-loop region. Inside, states are unstable, and as a result simulation of equilibrium phase coexistence becomes impossible. To overcome this problem, a method was proposed by Kim, Keyes and Straub [1], where optimally designed generalized ensemble sampling was combined with replica exchange, and denoted generalized replica exchange method (gREM). This new technique uses parametrized effective sampling weights that lead to a unimodal energy distribution, transforming unstable states into stable ones. In the present study, the gREM, originally developed as a Monte Carlo algorithm, was implemented to work with molecular dynamics in an isobaric ensemble and coded into LAMMPS, a highly optimized open source molecular simulation package. The method is illustrated in a study of the very strong solid/liquid transition in water.