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Randomized tree construction algorithm to explore energy landscapes
dc.contributor.author | Jaillet, Leonard Georges |
dc.contributor.author | Corcho Sánchez, Francisco José |
dc.contributor.author | Pérez González, Juan Jesús |
dc.contributor.author | Cortés, Juan |
dc.contributor.other | Universitat Politècnica de Catalunya. Departament d'Enginyeria Química |
dc.contributor.other | Institut de Robòtica i Informàtica Industrial |
dc.date.accessioned | 2012-01-10T18:27:45Z |
dc.date.available | 2012-01-10T18:27:45Z |
dc.date.created | 2011-12 |
dc.date.issued | 2011-12 |
dc.identifier.citation | Jaillet, L. [et al.]. Randomized tree construction algorithm to explore energy landscapes. "Journal of computational chemistry", Desembre 2011, vol. 32, núm. 16, p. 3464-3474. |
dc.identifier.issn | 0192-8651 |
dc.identifier.uri | http://hdl.handle.net/2117/14456 |
dc.description.abstract | We report in the present work a new method for exploring conformational energy landscapes. The method, called T-RRT, combines ideas from statistical physics and robot path planning algorithms. A search tree is constructed on the conformational space starting from a given state. The tree expansion is driven by a double strategy: on the one hand, it is naturally biased towards yet unexplored regions of the space; on the other, a Monte Carlo-like transition test guides the expansion toward energetically favorable regions. The balance between these two strategies is automatically achieved thanks to a self-tuning mechanism. The method is able to efficiently find both, energy minima and transition paths between them. As a proof of concept, the method is applied to two academic benchmarks and to the alanine dipeptide. |
dc.format.extent | 11 p. |
dc.language.iso | eng |
dc.rights | Attribution-NonCommercial-NoDerivs 3.0 Spain |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/3.0/es/ |
dc.subject.classification | Control predictiu |
dc.subject.lcsh | Predictive control |
dc.title | Randomized tree construction algorithm to explore energy landscapes |
dc.type | Article |
dc.subject.lemac | Àrees temàtiques de la UPC::Informàtica::Intel·ligència artificial artificial |
dc.contributor.group | Universitat Politècnica de Catalunya. ROBiri - Grup de Robòtica de l'IRI |
dc.contributor.group | Universitat Politècnica de Catalunya. ENGMOL - Enginyeria Molecular |
dc.identifier.doi | 10.1002/jcc.21931 |
dc.subject.inspec | Classificació INSPEC::Cybernetics::Artificial intelligence::Planning (artificial intelligence)::Path planning |
dc.rights.access | Open Access |
local.identifier.drac | 8599840 |
dc.description.version | Postprint (author’s final draft) |
local.citation.author | Jaillet, L.; Corcho, F.; Pérez, J.; Cortés, J. |
local.citation.publicationName | Journal of computational chemistry |
local.citation.volume | 32 |
local.citation.number | 16 |
local.citation.startingPage | 3464 |
local.citation.endingPage | 3474 |
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