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dc.contributor.authorAkhmatskaya E.en_US
dc.contributor.authorFernández-Pendás M.en_US
dc.contributor.authorRadivojevic T.
dc.contributor.authorSanz-Serna J. M.
dc.date.accessioned2017-08-04T10:32:06Z
dc.date.available2017-08-04T10:32:06Z
dc.date.issued2017-10-24
dc.identifier.issn0743-7463
dc.identifier.urihttp://hdl.handle.net/20.500.11824/715
dc.description.abstractThe modified Hamiltonian Monte Carlo (MHMC) methods, i.e., importance sampling methods that use modified Hamiltonians within a Hybrid Monte Carlo (HMC) framework, often outperform in sampling efficiency standard techniques such as molecular dynamics (MD) and HMC. The performance of MHMC may be enhanced further through the rational choice of the simulation parameters and by replacing the standard Verlet integrator with more sophisticated splitting algorithms. Unfortunately, it is not easy to identify the appropriate values of the parameters that appear in those algorithms. We propose a technique, that we call MAIA (Modified Adaptive Integration Approach), which, for a given simulation system and a given time step, automatically selects the optimal integrator within a useful family of two-stage splitting formulas. Extended MAIA (or e-MAIA) is an enhanced version of MAIA, which additionally supplies a value of the method-specific parameter that, for the problem under consideration, keeps the momentum acceptance rate at a user-desired level. The MAIA and e-MAIA algorithms have been implemented, with no computational overhead during simulations, in MultiHMC-GROMACS, a modified version of the popular software package GROMACS. Tests performed on well-known molecular models demonstrate the superiority of the suggested approaches over a range of integrators (both standard and recently developed), as well as their capacity to improve the sampling efficiency of GSHMC, a noticeable method for molecular simulation in the MHMC family. GSHMC combined with e-MAIA shows a remarkably good performance when compared to MD and HMC coupled with the appropriate adaptive integrators.en_US
dc.description.sponsorshipMTM2013-46553-C3-1-P MTM2016-77660-Pen_US
dc.formatapplication/pdfen_US
dc.language.isoengen_US
dc.publisherLangmuiren_US
dc.relationES/1PE/SEV-2013-0323en_US
dc.relationES/1PE/MTM2016-76329-Ren_US
dc.relationEUS/BERC/BERC.2014-2017en_US
dc.relationEUS/ELKARTEKen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.rights.urihttp://creativecommons.org/licenses/by-nc-sa/3.0/es/en_US
dc.subjectmolecular dynamicsen_US
dc.subjectHybrid Monte Carloen_US
dc.subjectimportance samplingen_US
dc.subjectmodified Hamiltoniansen_US
dc.subjectnumerical integratorsen_US
dc.titleAdaptive Splitting Integrators for Enhancing Sampling Efficiency of Modified Hamiltonian Monte Carlo Methods in Molecular Simulationen_US
dc.typeinfo:eu-repo/semantics/articleen_US
dc.typeinfo:eu-repo/semantics/publishedVersionen_US
dc.identifier.doi10.1021/acs.langmuir.7b01372
dc.relation.publisherversionhttp://pubs.acs.org/doi/abs/10.1021/acs.langmuir.7b01372en_US


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