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Issue 43, 2018
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Massive replica exchange Monte Carlo algorithm: a tool to access high pressure thermodynamics of hard systems

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Abstract

We have explored the idea of producing the equilibrium equation of state, i.e. the pressure as a function of packing fraction, βP(φ), of a confined system up to very high pressures to yield the configuration that leads to the maximum packing fraction. For this purpose we have massively implemented the replica exchange Monte Carlo algorithm in graphics processing units (GPUs), in such a way that each GPU core handles a single simulation cell. This yields a very easy scheme to implement parallelization for a very large amount of replicas (thousands), which densely sample configuration space. We have tested this idea with a very well studied system, i.e. discs confined in a circular cavity, for a number of particles N ≤ 125. In all cases, our outcomes for configurations having maximum packing fractions are in perfect agreement with those already reported and conjectured optimal in the literature, for which there is no formal mathematical proof, strongly suggesting that they are indeed optimal configurations. Furthermore, in most cases, we have obtained the same function βP(φ), by compressing loose random configurations and by decompressing copies of the configuration having the largest packing fraction. This reveals numerically that the so obtained maximum packing configurations are the correct answer.

Graphical abstract: Massive replica exchange Monte Carlo algorithm: a tool to access high pressure thermodynamics of hard systems

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Publication details

The article was received on 12 Jun 2018, accepted on 08 Oct 2018 and first published on 10 Oct 2018


Article type: Paper
DOI: 10.1039/C8CP03727B
Citation: Phys. Chem. Chem. Phys., 2018,20, 27490-27500
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    Massive replica exchange Monte Carlo algorithm: a tool to access high pressure thermodynamics of hard systems

    E. Basurto, C. Haro-Pérez, C. A. Vargas and G. Odriozola, Phys. Chem. Chem. Phys., 2018, 20, 27490
    DOI: 10.1039/C8CP03727B

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