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Modeling and enhanced sampling of molecular systems with smooth and nonlinear data-driven collective variables
dc.contributor.author | Hashemian, B. |
dc.contributor.author | Millán, Raúl Daniel |
dc.contributor.author | Arroyo Balaguer, Marino |
dc.contributor.other | Universitat Politècnica de Catalunya. Departament de Matemàtica Aplicada III |
dc.date.accessioned | 2013-12-09T13:02:47Z |
dc.date.available | 2013-12-09T13:02:47Z |
dc.date.created | 2013 |
dc.date.issued | 2013 |
dc.identifier.citation | Hashemian, B.; Millán, D.; Arroyo, M. Modeling and enhanced sampling of molecular systems with smooth and nonlinear data-driven collective variables. "Journal of chemical physics", 2013, vol. 139, p. 214101-1-214101-12. |
dc.identifier.issn | 0021-9606 |
dc.identifier.uri | http://hdl.handle.net/2117/20940 |
dc.description.abstract | Collective variables (CVs) are low-dimensional representations of the state of a complex system, which help us rationalize molecular conformations and sample free energy landscapes with molecular dynamics simulations. Given their importance, there is need for systematic methods that effectively identify CVs for complex systems. In recent years, nonlinear manifold learning has shown its ability to automatically characterize molecular collective behavior. Unfortunately, these methods fail to provide a differentiable function mapping high-dimensional configurations to their low-dimensional representation, as required in enhanced sampling methods. We introduce a methodology that, starting from an ensemble representative of molecular flexibility, builds smooth and nonlinear data-driven collective variables (SandCV) from the output of nonlinear manifold learning algorithms. We demonstrate the method with a standard benchmark molecule, alanine dipeptide, and show how it can be non-intrusively combined with off-the-shelf enhanced sampling methods, here the adaptive biasing force method. We illustrate how enhanced sampling simulations with SandCV can explore regions that were poorly sampled in the original molecular ensemble. We further explore the transferability of SandCV from a simpler system, alanine dipeptide in vacuum, to a more complex system, alanine dipeptide in explicit water. |
dc.language.iso | eng |
dc.subject | Àrees temàtiques de la UPC::Matemàtiques i estadística::Matemàtica aplicada a les ciències |
dc.subject.lcsh | Biology -- Classification -- Molecular aspects |
dc.title | Modeling and enhanced sampling of molecular systems with smooth and nonlinear data-driven collective variables |
dc.type | Article |
dc.subject.lemac | Molècules -- Models matemàtics |
dc.contributor.group | Universitat Politècnica de Catalunya. LACÀN - Mètodes Numèrics en Ciències Aplicades i Enginyeria |
dc.identifier.doi | 10.1063/1.4830403 |
dc.description.peerreviewed | Peer Reviewed |
dc.relation.publisherversion | http://scitation.aip.org/content/aip/journal/jcp/139/21/10.1063/1.4830403 |
dc.rights.access | Open Access |
local.identifier.drac | 12914254 |
dc.description.version | Postprint (published version) |
dc.relation.projectid | info:eu-repo/grantAgreement/EC/FP7/240487/EU/Predictive models and simulations in nano- and biomolecular mechanics: a multiscale approach/PREDMODSIM |
local.citation.author | Hashemian, B.; Millán, D.; Arroyo, M. |
local.citation.publicationName | Journal of chemical physics |
local.citation.volume | 139 |
local.citation.startingPage | 214101-1 |
local.citation.endingPage | 214101-12 |
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