Multicanonical jump walking: A method for efficiently sampling rough energy landscapes

1999 ◽  
Vol 110 (21) ◽  
pp. 10299-10306 ◽  
Author(s):  
Huafeng Xu ◽  
B. J. Berne
Keyword(s):  
Energy Landscapes ◽  
Rough Energy Landscapes

Thermodynamic Properties of Heisenberg Spin Systems

2019 ◽  
Vol 806 ◽  
pp. 142-154 ◽  
Author(s):  
Vitaliy Yu. Kapitan ◽  
Yuriy A. Shevchenko ◽  
Alexander V. Perzhu ◽  
Egor V. Vasiliev
Keyword(s):  
Heisenberg Model ◽  
Order Parameter ◽  
Exchange Interactions ◽  
Spin Systems ◽  
Energy Landscapes ◽  
Critical Temperatures ◽  
Heisenberg Spin ◽  
Simulation Results ◽  
Rough Energy Landscapes ◽  
2D And 3D

We present the simulation results of magnetic 2D and 3D structures with direct (for both of them) and Dzyaloshinskii-Moriya (DMI) (for 2D lattice) interactions in the frame of the Heisenberg model. We have adapted the multipath Metropolis algorithm for systems with complex types of exchange interactions and rough energy landscapes. We show the temperature behavior of magnetization, energy, and heat capacity, and reveal its critical temperatures and order parameter.

REVO: Resampling of Ensembles by Variation Optimization

2019 ◽  
Author(s):  
Nazanin Donyapour ◽  
Nicole Roussey ◽  
Alex Dickson
Keyword(s):  
Protein Interactions ◽  
Conformational Space ◽  
Energy Landscapes ◽  
Enhanced Sampling ◽  
Biomolecular Systems ◽  
Space Networks ◽  
Diverse Ensemble ◽  
Efficient Exploration ◽  
Dynamics Simulations ◽  
Rough Energy Landscapes

Conventional molecular dynamics simulations are incapable of sampling many important interactions in biomolecular systems due to their high dimensionality and rough energy landscapes. To observe rare events and calculate transition rates in these systems, enhanced sampling is a necessity. In particular, the study of ligand-protein interactions necessitates a diverse ensemble of protein conformations and transition states, and for many systems this occurs on prohibitively long timescales. Previous strategies such as WExplore that can be used to determine these types of ensembles are hindered by problems related to the regioning of conformational space. Here we propose a novel, regionless, enhanced sampling method that is based on the weighted ensemble framework. In this method, a value referred to as “trajectory variation” is optimized after each cycle through cloning and merging operations. This method allows for a more consistent measurement of observables and broader sampling resulting in the efficient exploration of previously unexplored conformations. We demonstrate the performance of this algorithm with the N-dimensional random walk and the unbinding of the trypsin-benzamidine system. The system is analyzed using conformation space networks, the residence time of benzamidine is confirmed, and a new unbinding pathway for the trypsin-benzamidine system is found. We expect that REVO will be a useful general tool to broadly explore free energy landscapes.

REVO: Resampling of Ensembles by Variation Optimization

2019 ◽  
Author(s):  
Nazanin Donyapour ◽  
Nicole Roussey ◽  
Alex Dickson
Keyword(s):  
Protein Interactions ◽  
Conformational Space ◽  
Energy Landscapes ◽  
Enhanced Sampling ◽  
Biomolecular Systems ◽  
Space Networks ◽  
Diverse Ensemble ◽  
Efficient Exploration ◽  
Dynamics Simulations ◽  
Rough Energy Landscapes

Conventional molecular dynamics simulations are incapable of sampling many important interactions in biomolecular systems due to their high dimensionality and rough energy landscapes. To observe rare events and calculate transition rates in these systems, enhanced sampling is a necessity. In particular, the study of ligand-protein interactions necessitates a diverse ensemble of protein conformations and transition states, and for many systems this occurs on prohibitively long timescales. Previous strategies such as WExplore that can be used to determine these types of ensembles are hindered by problems related to the regioning of conformational space. Here we propose a novel, regionless, enhanced sampling method that is based on the weighted ensemble framework. In this method, a value referred to as “trajectory variation” is optimized after each cycle through cloning and merging operations. This method allows for a more consistent measurement of observables and broader sampling resulting in the efficient exploration of previously unexplored conformations. We demonstrate the performance of this algorithm with the N-dimensional random walk and the unbinding of the trypsin-benzamidine system. The system is analyzed using conformation space networks, the residence time of benzamidine is confirmed, and a new unbinding pathway for the trypsin-benzamidine system is found. We expect that REVO will be a useful general tool to broadly explore free energy landscapes.

Efficient sampling over rough energy landscapes with high barriers: A combination of metadynamics with integrated tempering sampling

2016 ◽  
Vol 144 (9) ◽  
pp. 094105 ◽  
Author(s):  
Y. Isaac Yang ◽  
Jun Zhang ◽  
Xing Che ◽  
Lijiang Yang ◽  
Yi Qin Gao
Keyword(s):  
Energy Landscapes ◽  
Efficient Sampling ◽  
Rough Energy Landscapes

scholarly journals Sampling from rough energy landscapes

2020 ◽  
Vol 18 (8) ◽  
pp. 2271-2303
Author(s):  
Petr Plecháč ◽  
Gideon Simpson
Keyword(s):  
Energy Landscapes ◽  
Rough Energy Landscapes

REVO: Resampling of Ensembles by Variation Optimization

2019 ◽  
Author(s):  
Nazanin Donyapour ◽  
Nicole Roussey ◽  
Alex Dickson
Keyword(s):  
Protein Interactions ◽  
Conformational Space ◽  
Energy Landscapes ◽  
Enhanced Sampling ◽  
Biomolecular Systems ◽  
Space Networks ◽  
Diverse Ensemble ◽  
Efficient Exploration ◽  
Dynamics Simulations ◽  
Rough Energy Landscapes

Conventional molecular dynamics simulations are incapable of sampling many important interactions in biomolecular systems due to their high dimensionality and rough energy landscapes. To observe rare events and calculate transition rates in these systems, enhanced sampling is a necessity. In particular, the study of ligand-protein interactions necessitates a diverse ensemble of protein conformations and transition states, and for many systems this occurs on prohibitively long timescales. Previous strategies such as WExplore that can be used to determine these types of ensembles are hindered by problems related to the regioning of conformational space. Here we propose a novel, regionless, enhanced sampling method that is based on the weighted ensemble framework. In this method, a value referred to as “trajectory variation” is optimized after each cycle through cloning and merging operations. This method allows for a more consistent measurement of observables and broader sampling resulting in the efficient exploration of previously unexplored conformations. We demonstrate the performance of this algorithm with the N-dimensional random walk and the unbinding of the trypsin-benzamidine system. The system is analyzed using conformation space networks, the residence time of benzamidine is confirmed, and a new unbinding pathway for the trypsin-benzamidine system is found. We expect that REVO will be a useful general tool to broadly explore free energy landscapes.

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