Suscripción a Biblioteca: Guest
International Journal for Uncertainty Quantification

Publicado 6 números por año

ISSN Imprimir: 2152-5080

ISSN En Línea: 2152-5099

The Impact Factor measures the average number of citations received in a particular year by papers published in the journal during the two preceding years. 2017 Journal Citation Reports (Clarivate Analytics, 2018) IF: 1.7 To calculate the five year Impact Factor, citations are counted in 2017 to the previous five years and divided by the source items published in the previous five years. 2017 Journal Citation Reports (Clarivate Analytics, 2018) 5-Year IF: 1.9 The Immediacy Index is the average number of times an article is cited in the year it is published. The journal Immediacy Index indicates how quickly articles in a journal are cited. Immediacy Index: 0.5 The Eigenfactor score, developed by Jevin West and Carl Bergstrom at the University of Washington, is a rating of the total importance of a scientific journal. Journals are rated according to the number of incoming citations, with citations from highly ranked journals weighted to make a larger contribution to the eigenfactor than those from poorly ranked journals. Eigenfactor: 0.0007 The Journal Citation Indicator (JCI) is a single measurement of the field-normalized citation impact of journals in the Web of Science Core Collection across disciplines. The key words here are that the metric is normalized and cross-disciplinary. JCI: 0.5 SJR: 0.584 SNIP: 0.676 CiteScore™:: 3 H-Index: 25

Indexed in

CALIBRATION AND RANKING OF COARSE-GRAINED MODELS IN MOLECULAR SIMULATIONS USING BAYESIAN FORMALISM

Volumen 7, Edición 2, 2017, pp. 99-115
DOI: 10.1615/Int.J.UncertaintyQuantification.2017013407
Get accessGet access

SINOPSIS

Understanding and prediction of the performance of complex materials using molecular simulations, as well as the design of a new generation of materials with desired functionality, depend on the predictive capacity of the coarsegrained models that enable reduced computational cost. Depending on what aspect of the behavior is of interest, we often have at our disposal various coarse-grained models with different predictive capabilities. In this work, focusing on coarse-grained water models, we demonstrate how the plausibilities of these models are relatively compared, and how predictions can be made exploiting the ensemble. Using a Bayesian model ranking framework, we will show the plausibility results which are in agreement with experts' expectation on how these models rank in terms of predicting different quantities of interest, and how different models can be mixed and produce an ensemble prediction with higher accuracy.

CITADO POR
  1. Asaadi Erfan, Heyns P. Stephan, Haftka Raphael T., Tootkaboni Mazdak, On the value of test data for reducing uncertainty in material models: Computational framework and application to spherical indentation, Computer Methods in Applied Mechanics and Engineering, 346, 2019. Crossref

  2. Cailliez Fabien, Pernot Pascal, Rizzi Francesco, Jones Reese, Knio Omar, Arampatzis Georgios, Koumoutsakos Petros, Bayesian calibration of force fields for molecular simulations, in Uncertainty Quantification in Multiscale Materials Modeling, 2020. Crossref

  3. Razi M., Narayan A., Kirby R.M., Bedrov D., Force-field coefficient optimization of coarse-grained molecular dynamics models with a small computational budget, Computational Materials Science, 176, 2020. Crossref

Portal Digitalde Biblioteca Digital eLibros Revistas Referencias y Libros de Ponencias Colecciones Precios y Políticas de Suscripcione Begell House Contáctenos Language English 中文 Русский Português German French Spain