Computational Strategies for Mapping Equilibrium Phase Diagrams

Bruce, Ann; Wilding, Nigel B.

doi:10.1002/chin.200345217

Cited by 14 publications

(32 citation statements)

References 73 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…10,11 ) Our method corresponds to a multi-level implementation with only two levels, so we call it a two-level (TL) method. It shares many features with importance sampling methods, biased sampling techniques, 22,23 and free-energy perturbation methods 24 (see for example Ref. 15 for a recent application of this method, in order to correct for coarse-graining errors).…”

Section: Introductionmentioning

confidence: 99%

Correction of coarse-graining errors by a two-level method: Application to the Asakura-Oosawa model

Kobayashi

Rohrbach

Scheichl

et al. 2019

The Journal of Chemical Physics

Self Cite

View full text Add to dashboard Cite

We present a method that exploits self-consistent simulation of coarse-grained and fine-grained models, in order to analyse properties of physical systems. The method uses the coarse-grained model to obtain a first estimate of the quantity of interest, before computing a correction by analysing properties of the fine system. We illustrate the method by applying it to the Asakura-Oosawa (AO) model of colloid-polymer mixtures. We show that the liquid-vapour critical point in that system is affected by three-body interactions which are neglected in the corresponding coarse-grained model. We analyse the size of this effect and the nature of the three-body interactions. We also analyse the accuracy of the method, as a function of the associated computational effort. arXiv:1907.07912v1 [cond-mat.stat-mech]

show abstract

Section: Introductionmentioning

confidence: 99%

Correction of coarse-graining errors by a two-level method: Application to the Asakura-Oosawa model

Kobayashi

Rohrbach

Scheichl

et al. 2019

The Journal of Chemical Physics

Self Cite

View full text Add to dashboard Cite

show abstract

“…Then the relative stability of fluid (F) and crystalline solid (CS) phases is determined by the difference in their Gibbs free energies. This can in turn be obtained from the ratio of the a-priori probabilities of the phases [1,5]:…”

Section: A Monodisperse Systemsmentioning

confidence: 99%

“…To achieve this one generally aims to engineer a sampling path which connects the phases in question, allowing each to be visited repeatedly in the course of a single simulation run. The reward for doing so comes in the form of direct, accurate and transparent measurements of free energy differences and coexistence parameters [1].…”

Section: Introductionmentioning

confidence: 99%

“…Typically such regions of configuration space are characterized by large free energy barriers, or by a 'pitted' free energy landscape -features which hinder efficient sampling. Contemporary strategies seek to either surmount or circumvent these impediments (for a review see [1]). One such scheme -which is in principle rather general -is phase switch Monte Carlo (PSMC).…”

Section: Introductionmentioning

confidence: 99%

“…To describe fractionation it is necessary to define separate "daughter" distributions ρ (i) (σ) ( i = 1, 2...) which measure the distribution of the polydisperse attribute for each phase i. When the polydispersity is fixed, conservation of particles implies that the weighted average of the daughter distributions equals the fixed overall density distribution, or "parent" ρ (0) (σ), For instance, at two-phase coexistence ρ (0) (σ) = n (0) f (σ) = (1 − ξ)ρ (1) (σ) + ξρ (2) (σ), with 1−ξ and ξ the respective fractional volumes of the phases. This expression represents a generalisation of the Lever rule to polydisperse systems.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Solid-liquid coexistence of polydisperse fluids via simulation

Wilding

2009

The Journal of Chemical Physics

Self Cite

View full text Add to dashboard Cite

We describe a simulation method for the accurate study of the equilibrium freezing properties of polydisperse fluids under the experimentally relevant condition of fixed polydispersity. The approach is based on the phase switch Monte Carlo method of Wilding and Bruce [Phys. Rev. Lett. 85, 5138 (2000)]. This we have generalized to deal with particle size polydispersity by incorporating updates which alter the diameter sigma of a particle, under the control of a distribution of chemical potential differences mu(sigma). Within the resulting isobaric semi-grand-canonical ensemble, we detail how to adapt mu(sigma) and the applied pressure such as to study coexistence, while ensuring that the ensemble averaged density distribution rho(sigma) matches a fixed functional form. Results are presented for the effects of small degrees of polydispersity on the solid-liquid transition of soft spheres.

show abstract

Extension of the Aggregation-Volume-Bias Monte Carlo Method to the Calculation of Phase Properties of Solid Systems: A Lattice-Based Cluster Approach

Chen

2022

J. Phys. Chem. A

View full text Add to dashboard Cite

The aggregation-volume-bias Monte Carlo method, which has been successful in the calculation of the formation free energies of liquid clusters, is extended to solid systems. This extension is motivated by early studies where disordered clusters are observed when the original method is applied at a temperature even far below the triple point. In order to avoid the formation of disordered aggregates, the insertion of particles is targeted directly toward those crystal lattice sites. Specifically, the insertion volume used to be defined as a spherical volume centered around a given target molecule is now restricted to be around each of the crystal lattice sites near a given target molecule. The free energies obtained for both liquid and solid clusters are then used to extrapolate bulk-phase information such as the chemical potential of the liquid and solid phases at coexistence. Using the temperature and pressure dependencies of the chemical potential information obtained for both liquid and solid phases, the location of the triple point can be determined. For Lennard-Jonesium, the results were found to be in good agreement with previous simulation studies using other approaches.

show abstract

Computational Strategies for Mapping Equilibrium Phase Diagrams

Abstract: For Abstract see ChemInform Abstract in Full Text.

Cited by 14 publications

References 73 publications

Correction of coarse-graining errors by a two-level method: Application to the Asakura-Oosawa model

Correction of coarse-graining errors by a two-level method: Application to the Asakura-Oosawa model

Solid-liquid coexistence of polydisperse fluids via simulation

Extension of the Aggregation-Volume-Bias Monte Carlo Method to the Calculation of Phase Properties of Solid Systems: A Lattice-Based Cluster Approach

Contact Info

Product

Resources

About