scholarly journals Driving Structural Transitions in Molecular Simulations Using the Nonequilibrium Candidate Monte Carlo

2017 ◽  
Vol 122 (3) ◽  
pp. 1195-1204 ◽  
Author(s):  
Anıl Kurut ◽  
Rasmus Fonseca ◽  
Wouter Boomsma
Keyword(s):  
Monte Carlo ◽  
Molecular Simulations ◽  
Structural Transitions

scholarly journals Molecular Simulations of CO2/CH4, CO2/N2 and N2/CH4 Binary Mixed Hydrates

2021 ◽  
Vol 83 (3) ◽  
pp. 372-378
Author(s):  
A. A. Sizova ◽  
S. A. Grintsevich ◽  
M. A. Kochurin ◽  
V. V. Sizov ◽  
E. N. Brodskaya
Keyword(s):  
Carbon Dioxide ◽  
Monte Carlo ◽  
Monte Carlo Simulations ◽  
Gas Hydrates ◽  
Molecular Simulations ◽  
Gas Mixtures ◽  
Gas Mixture ◽  
Grand Canonical Monte Carlo ◽  
Multicomponent Gas ◽  
Grand Canonical

Abstract Grand canonical Monte Carlo simulations were performed to study the occupancy of structure I multicomponent gas hydrates by CO2/CH4, CO2/N2, and N2/CH4 binary gas mixtures with various compositions at a temperature of 270 K and pressures up to 70 atm. The presence of nitrogen in the gas mixture allows for an increase of both the hydrate framework selectivity to CO2 and the amount of carbon dioxide encapsulated in hydrate cages, as compared to the CO2/CH4 hydrate. Despite the selectivity to CH4 molecules demonstrated by N2/CH4 hydrate, nitrogen can compete with methane if the gas mixture contains at least 70% of N2.

scholarly journals Solution structure of macromolecules using small angle neutron scattering and molecular simulations

2020 ◽  
Vol 236 ◽  
pp. 03003
Author(s):  
Jayesh S. Bhatt
Keyword(s):  
Molecular Dynamics ◽  
Monte Carlo ◽  
Neutron Scattering ◽  
Small Angle ◽  
Small Angle Neutron Scattering ◽  
Solution Structure ◽  
Molecular Simulations ◽  
Liquid Solution ◽  
Scattering Data ◽  
Monte Carlo Techniques

An introductory account of using molecular simulations to deduce solution structure of macromolecules using small angle neutron scattering data is presented for biologists. The presence of a liquid solution provides mobility to the molecules, making it difficult to pin down their structure. Here a simple introduction to molecular dynamics and Monte Carlo techniques is followed by a recipe to use the output of the simulations along with the scattering data in order to infer the structure of macromolecules when they are placed in a liquid solution. Some practical issues to be watched for are also highlighted.

scholarly journals Combining stochastic and deterministic approaches within high efficiency molecular simulations

2013 ◽  
Vol 11 (4) ◽  
Author(s):  
Bruno Escribano ◽  
Elena Akhmatskaya ◽  
Jon Mujika
Keyword(s):  
Molecular Dynamics ◽  
Monte Carlo ◽  
High Efficiency ◽  
Rare Event ◽  
Molecular Simulations ◽  
Monte Carlo Sampling ◽  
Symplectic Integrators ◽  
Computational Time ◽  
Hybrid Monte Carlo ◽  
Event Dynamics

AbstractGeneralized Shadow Hybrid Monte Carlo (GSHMC) is a method for molecular simulations that rigorously alternates Monte Carlo sampling from a canonical ensemble with integration of trajectories using Molecular Dynamics (MD). While conventional hybrid Monte Carlo methods completely re-sample particle’s velocities between MD trajectories, our method suggests a partial velocity update procedure which keeps a part of the dynamic information throughout the simulation. We use shadow (modified) Hamiltonians, the asymptotic expansions in powers of the discretization parameter corresponding to timestep, which are conserved by symplectic integrators to higher accuracy than true Hamiltonians. We present the implementation of this method into the highly efficient MD code GROMACS and demonstrate its performance and accuracy on computationally expensive systems like proteins in comparison with the molecular dynamics techniques already available in GROMACS. We take advantage of the state-of-the-art algorithms adopted in the code, leading to an optimal implementation of the method. Our implementation introduces virtually no overhead and can accurately recreate complex biological processes, including rare event dynamics, saving much computational time compared with the conventional simulation methods.

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