Monte Carlo cluster algorithm for fluid phase transitions in highly size-asymmetrical binary mixtures
Douglas J. Ashton, Jiwen Liu, Erik Luijten, Nigel B. Wilding

TL;DR
This paper introduces an efficient Monte Carlo cluster algorithm embedded in a restricted Gibbs ensemble to study fluid phase transitions in highly size-asymmetrical binary mixtures, enabling accurate analysis of phase behavior.
Contribution
The authors develop and detail a rejection-free geometrical cluster algorithm for simulating highly size-asymmetrical mixtures within a restricted Gibbs ensemble, improving simulation efficiency and accuracy.
Findings
Adding small particles decreases the critical temperature by ~50%.
Critical density drops by ~30% with small particle addition.
The method reveals that small particles reduce net attraction between large particles.
Abstract
Highly size-asymmetrical fluid mixtures arise in a variety of physical contexts, notably in suspensions of colloidal particles to which much smaller particles have been added in the form of polymers or nanoparticles. Conventional schemes for simulating models of such systems are hamstrung by the difficulty of relaxing the large species in the presence of the small one. Here we describe how the rejection-free geometrical cluster algorithm (GCA) of Liu and Luijten [Phys. Rev. Lett 92, 035504 (2004)] can be embedded within a restricted Gibbs ensemble to facilitate efficient and accurate studies of fluid phase behavior of highly size-asymmetrical mixtures. After providing a detailed description of the algorithm, we summarize the bespoke analysis techniques of Ashton et al. [J. Chem. Phys. 132, 074111 (2010)] that permit accurate estimates of coexisting densities and critical-point…
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