2010
DOI: 10.1063/1.3314289
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Alcohol solubility in a lipid bilayer: Efficient grand-canonical simulation of an interfacially active molecule

Abstract: We derive a new density-biased Monte Carlo technique which preserves detailed balance and improves the convergence of grand-canonical simulations of a species with a strong preference for an interfacial region as compared to the bulk. This density-biasing technique is applied to the solubility of "alcohol" molecules in a mesoscopic model of the lipid bilayer, a system which has anesthetic implications but is poorly understood.

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Cited by 12 publications
(29 citation statements)
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“…A similar technique has been utilized in the lipid bilayer to improve the convergence of MC simulations. 25 The Metropolis criteria were changed to account for the modified insertion/deletion algorithm that no longer samples uniformly from the entire simulation volume to preserve detailed balance where the details are similar to what is shown elsewhere. 25 To eliminate possible biases, the reduced volume is uniformly sampled during the insertion moves and not just at the specific low-energy grid points.…”
Section: ■ Grand Canonical Monte Carlo Methods For Porous Materialsmentioning
confidence: 99%
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“…A similar technique has been utilized in the lipid bilayer to improve the convergence of MC simulations. 25 The Metropolis criteria were changed to account for the modified insertion/deletion algorithm that no longer samples uniformly from the entire simulation volume to preserve detailed balance where the details are similar to what is shown elsewhere. 25 To eliminate possible biases, the reduced volume is uniformly sampled during the insertion moves and not just at the specific low-energy grid points.…”
Section: ■ Grand Canonical Monte Carlo Methods For Porous Materialsmentioning
confidence: 99%
“…25 The Metropolis criteria were changed to account for the modified insertion/deletion algorithm that no longer samples uniformly from the entire simulation volume to preserve detailed balance where the details are similar to what is shown elsewhere. 25 To eliminate possible biases, the reduced volume is uniformly sampled during the insertion moves and not just at the specific low-energy grid points. In our algorithm, we choose two CO 2 energy cutoff values, E cut,1 and E cut,2 (where E cut,1 = 5k B T + E min and E cut,2 = 300k B T + E min , with E min indicating the global minimum energy point), to accelerate convergence.…”
Section: ■ Grand Canonical Monte Carlo Methods For Porous Materialsmentioning
confidence: 99%
“…Old NPγT Moves. In our earlier work using the NPγT ensemble, 9 we developed Monte Carlo moves based on the partition function:…”
Section: Monte Carlo Moves In the Npγt Ensemblementioning
confidence: 99%
“…• applying a pressure P ⊥ normal to the bilayer and zero surface tension γ in the plane of the bilayer (NPγT) 6,8,9 or • applying the same pressure normal to the bilayer and in the plane of the bilayer so that P ⊥ = P ∥ yet the shape is decoupled in the normal and lateral directions (NP ∥ P ⊥ T) 7 Both approaches allow for the needed shape changes as lipid bilayers vary temperature and move into different phases. However, the equivalence of the NPγT scheme and the NP ∥ P ⊥ T scheme for simulating tensionless bilayers is not proven.…”
Section: Introductionmentioning
confidence: 99%
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