Convergence estimation of flat-histogram algorithms based on simulation results

Shakirov, Timur

doi:10.1016/j.cpc.2018.03.009

Cited by 8 publications

(12 citation statements)

References 22 publications

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“…This estimate is updated after each step as where x new = x ′ if the move is accepted and x new = x if it is rejected. The modification factor γ t , where t is time measured in the number of move trials, has to go to 0 algebraically for t → ∞ (for more details, see refs and ).…”

Section: Theoretical Methodsmentioning

confidence: 99%

Thermodynamics and Conformations of Single Polyalanine, Polyserine, and Polyglutamine Chains within the PRIME20 Model

Böker

Paul

2022

J. Phys. Chem. B

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Understanding the conformational behavior of proteins has been a long-standing challenge to which computer simulations could contribute significantly. This concerns the folding behavior of proteins as well as the conformational statistics of intrinsically disordered proteins. A well-converged sampling of the conformational statistics over a broad range of control parameters is computationally extremely challenging and best addressed by coarse-grained modeling, for example, with an intermediate resolution model like the PRIME20 model. A comprehensive understanding of the thermodynamics and conformational statistics of individual protein chains is, however, not only a goal in itself but also a prerequisite for a better understanding of their aggregation tendency into oligomers and further into amyloid fibrils. We present here an extensive comparison of the ability of the PRIME20 model (in its documented variants in the literature) to faithfully reproduce the thermodynamics and statistics of three homopeptides having very different folding temperatures and different typical secondary structures.

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Section: Theoretical Methodsmentioning

confidence: 99%

Thermodynamics and Conformations of Single Polyalanine, Polyserine, and Polyglutamine Chains within the PRIME20 Model

Böker

Paul

2022

J. Phys. Chem. B

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“…We numerically estimate the entropy,

, of the system by the SAMC method [ 15 , 16 , 17 , 18 ]. The micro-canonical entropy defines the logarithm of the density of states (DOS) of the system

.…”

Section: Methodsmentioning

confidence: 99%

“…The micro-canonical entropy defines the logarithm of the density of states (DOS) of the system

. The SAMC method is a variant of the Wang–Landau algorithm having, in contrast to the original procedure, proven convergence to the exact DOS [ 16 , 17 ]. When the micro-canonical entropy is known, we can calculate any thermodynamic property of the system after accumulation of corresponding averages during a productive run with the fixed DOS [ 18 ].…”

Section: Methodsmentioning

confidence: 99%

“…The semi-flexible tangent hard-sphere chain is a simplified model of real polymers. We calculated previously, by means of stochastic approximation Monte Carlo (SAMC) simulation [ 15 , 16 , 17 ], the micro-canonical entropy of a dense semi-flexible tangent hard-sphere polymer system [ 18 ] (we provide a more detailed description of the model in Section 2 ). When the system is big enough, the model polymer melt undergoes an ordering transition into a layered rotator-like phase, which is similar to the rotator phases of n-alkanes: a family of smectically ordered phases with different ordering in layers [ 19 , 20 , 21 , 22 ].…”

Section: Introductionmentioning

confidence: 99%

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Crystallisation in Melts of Short, Semi-Flexible Hard-Sphere Polymer Chains: The Role of the Non-Bonded Interaction Range

Shakirov

2019

Entropy

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A melt of short semi-flexible polymers with hard-sphere-type non-bonded interaction undergoes a first-order crystallisation transition at lower density than a melt of hard-sphere monomers or a flexible hard-sphere chain. In contrast to the flexible hard-sphere chains, the semi-flexible ones have an intrinsic stiffness energy scale, which determines the natural temperature scale of the system. In this paper, we investigate the effect of weak additional non-bonded interaction on the phase transition temperature. We study the system using the stochastic approximation Monte Carlo (SAMC) method to estimate the micro-canonical entropy of the system. Since the density of states in the purely hard-sphere non-bonded interaction case already covers 5600 orders of magnitude, we consider the effect of weak interactions as a perturbation. In this case, the system undergoes the same ordering transition with a temperature shift non-uniformly depending on the additional interaction. Short-range attractions impede ordering of the melt of semi-flexible polymers and decrease the transition temperature, whereas relatively long-range attractions assist ordering and shift the transition temperature to higher values, whereas weak repulsive interactions demonstrate an opposite effect on the transition temperature.

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“…where g(E) is the multiplicity of the system which is proportional to D(E). We can estimate the average deviation from the exact solution using the relation [29][30][31]:…”

Section: Ising Modelmentioning

confidence: 99%

Flat histogram method comparison on 2D Ising Model

Pommerenck,

Roundy

2020

Preprint

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Convergence estimation of flat-histogram algorithms based on simulation results

Cited by 8 publications

References 22 publications

Thermodynamics and Conformations of Single Polyalanine, Polyserine, and Polyglutamine Chains within the PRIME20 Model

Thermodynamics and Conformations of Single Polyalanine, Polyserine, and Polyglutamine Chains within the PRIME20 Model

Crystallisation in Melts of Short, Semi-Flexible Hard-Sphere Polymer Chains: The Role of the Non-Bonded Interaction Range

Flat histogram method comparison on 2D Ising Model

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