KUTE: Green-Kubo Uncertainty-Based Transport Coefficient Estimator

dc.contributor.affiliationUniversidade de Santiago de Compostela. Instituto de Materiais (iMATUS)
dc.contributor.authorOtero-Lema, Martín
dc.contributor.authorLois-Cuns, Raúl
dc.contributor.authorBoado Fernández, Miguel Ángel
dc.contributor.authorMontes-Campos, Hadrián
dc.contributor.authorMéndez-Morales, Trinidad
dc.contributor.authorVarela Cabo, Luis Miguel
dc.date.accessioned2025-09-16T10:49:56Z
dc.date.available2025-09-16T10:49:56Z
dc.date.issued2025-03-24
dc.description.abstractAn algorithm for the calculation of transport properties from molecular dynamics simulations, kute, is introduced. The method estimates the integrals from the Green-Kubo theorem, taking into account the uncertainties of the correlation functions in order to eliminate arbitrary cutoffs or external parameters whose values could alter the result. In this contribution, the performance of kute is tested against other popular methods for the case of a protic ionic liquid for a variety of transport properties. It is found that kute achieves the same degree of accuracy as the equivalent formulation of the Einstein relations while performing better than other methods to calculate transport properties using Green-Kubo methods.
dc.description.peerreviewedSI
dc.description.sponsorshipThe financial support of the Spanish Ministry of Science and Innovation (PID2021-126148NA-I00 funded by MICIU/AEI/10.13039/501100011033/FEDER, UE) is gratefully acknowledged. Moreover, this work was funded by the Xunta de Galicia (GRC ED431C 2024/06). M.O.L. thanks the Xunta de Galicia for his “Axudas de apoio á etapa predoutoral” grant (ED481A 2022/236). This work was carried out within the framework of project HI_MOV “Corredor Tecnológico Transfronterizo de Movilidad con Hidrógeno Renovable”, with reference 0160_HI_MOV_1_E, cofinanced by the European Regional Development fund (ERDF), in the scope of Interreg VI A Spain Portugal Cooperation Program (POCTEP) 2021−2027. This publication and the contract of T.M.M. are part of the grant RYC2022-036679-I, funded by MICIU/AEI/10.13039/501100011033 and FSE+. This work is part of the project CNS2023-144785, funded by MICIU/ AEI/10.13039/501100011033 and the European Union “NextGenerationEU”/PRTR. H.M.C. thanks the USC for his “Convocatoria de Recualificación do Sistema Universitario Español-Margarita Salas” postdoctoral grant under the “Plan de Recuperación Transformación” program funded by the Spanish Ministry of Universities with European Union’s NextGenerationEU funds. R.L.C. acknowledges his Predoctoral Contract under the framework of the project PID2021- 126148NA-I00 funded by MICIU/AEI/10.13039/ 501100011033/FEDER, UE.
dc.identifier.citationOtero-Lema M, Lois-Cuns R, Boado MA, Montes-Campos H, Méndez-Morales T, Varela LM. KUTE: Green-Kubo Uncertainty-Based Transport Coefficient Estimator. J Chem Inf Model. 2025 Apr 14;65(7):3477-3487. doi: 10.1021/acs.jcim.4c02219. Epub 2025 Mar 19. PMID: 40105208; PMCID: PMC12124720.
dc.identifier.doi10.1021/acs.jcim.4c02219
dc.identifier.issn1549-9596
dc.identifier.urihttps://hdl.handle.net/10347/42828
dc.issue.number7
dc.journal.titleJournal of Chemical Information and Modeling (JCIM)
dc.language.isoeng
dc.page.final3487
dc.page.initial3477
dc.publisherAmerican Chemical Society
dc.relation.projectIDinfo:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2021-2023/PID2021-126148NA-I00/ES/SIMULACIONES DE ELECTROLITOS DENSAMENTE IONICOS SOMETIDOS A CONFINAMIENTO MEDIANTE POTENCIALES DE MACHINE LEARNING/
dc.relation.publisherversionhttps://doi.org/10.1021/acs.jcim.4c02219
dc.rightsThis article is licensed under CC-BY 4.0
dc.rightsAttribution 4.0 Internationalen
dc.rights.accessRightsopen access
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.titleKUTE: Green-Kubo Uncertainty-Based Transport Coefficient Estimator
dc.typejournal article
dc.type.hasVersionVoR
dc.volume.number65
dspace.entity.typePublication
relation.isAuthorOfPublication78f7e837-a983-40db-89f2-5363070f31bc
relation.isAuthorOfPublication697e8aad-c448-4fb2-8c2f-420f8cbdd517
relation.isAuthorOfPublication137dedc2-ea57-4cd6-b5bc-94b55d9d8b98
relation.isAuthorOfPublication.latestForDiscovery78f7e837-a983-40db-89f2-5363070f31bc

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