2019
DOI: 10.1063/1.5080431
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Configurational mapping significantly increases the efficiency of solid-solid phase coexistence calculations via molecular dynamics: Determining the FCC-HCP coexistence line of Lennard-Jones particles

Abstract: In this study, we incorporate configuration mapping between simulation ensembles into the successive interpolation of multistate reweighting (SIMR) method in order to increase phase space overlap between neighboring simulation ensembles. This significantly increases computational efficiency over the original SIMR method in many situations. We use this approach to determine the coexistence curve of FCC-HCP Lennard-Jones spheres using direct molecular dynamics and SIMR. As previously noted, the coexistence curve… Show more

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Cited by 6 publications
(6 citation statements)
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“…We explored several simple strategies for mapping common parameters between models, described below and illustrated in Figure , although others exist, such as non-equilibrium candidate Monte Carlo . This process is equivalent to mapping between configurational spaces in free energy perturbation. Direct mapping: In the simplest cases, parameters can be taken directly from one model and plugged into the other model. This is only useful in the case where the distributions of the parameters in the two models overlap significantly. Maximum a posteriori (MAP) mapping: Another possibility is to run a short MCMC simulation of each model and then map the highest probability (MAP) values of each common parameter to each other, either by translation (additive mapping) or rescaling (multiplicative mapping).…”
Section: Methodsmentioning
confidence: 99%
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“…We explored several simple strategies for mapping common parameters between models, described below and illustrated in Figure , although others exist, such as non-equilibrium candidate Monte Carlo . This process is equivalent to mapping between configurational spaces in free energy perturbation. Direct mapping: In the simplest cases, parameters can be taken directly from one model and plugged into the other model. This is only useful in the case where the distributions of the parameters in the two models overlap significantly. Maximum a posteriori (MAP) mapping: Another possibility is to run a short MCMC simulation of each model and then map the highest probability (MAP) values of each common parameter to each other, either by translation (additive mapping) or rescaling (multiplicative mapping).…”
Section: Methodsmentioning
confidence: 99%
“…The warped bridge sampling approach involves using bridge sampling on a set of samples from two models and then using RJMC proposal distributions and Gaussian affine mappings to “warp” the distributions to improve overlap, similar to mappings in configurational space. This technique can be seen as MBAR with a coordinate transformation. , …”
Section: Methodsmentioning
confidence: 99%
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“…The warped bridge sampling approach involves using bridge sampling on a set of samples from two models, then using the proposal distributions from section 2.4.2 and parameter mappings from section 2.4.3 to "warp" the distributions to improve overlap, similar to mappings in configurational space. [66,67].…”
Section: Model Distributions From Rjmcmentioning
confidence: 99%
“…Take, as an example, the TIP3P and SPC models which have different rigid bond lengths for the OH and HH bonds; bond lengths corresponding to one water model can never be sampled during the simulation of the other water model. We have shown one can efficiently capture the free energy differences between water models by creating phase space overlap between these states by mapping between configurations, 13,[17][18][19] and including the Jacobians of the these mapping transformations in the free energy calculations. We have laid out specifically how to carry out this process in the case of mapping between rigid water models in our previous work, 13 which we also review in the Methods section here.…”
Section: Introductionmentioning
confidence: 99%