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International Journal for Multiscale Computational Engineering

Publicado 6 números por año

ISSN Imprimir: 1543-1649

ISSN En Línea: 1940-4352

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: 1.4 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: 1.3 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: 2.2 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: 0.00034 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: 0.46 SJR: 0.333 SNIP: 0.606 CiteScore™:: 3.1 H-Index: 31

Indexed in

Numerical Assessment of Theoretical Error Estimates in Coarse-Grained Kinetic Monte Carlo Simulations: Application to Surface Diffusion

Volumen 3, Edición 1, 2005, pp. 59-70
DOI: 10.1615/IntJMultCompEng.v3.i1.50
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SINOPSIS

A coarse-grained kinetic Monte Carlo (CG-KMC) method was recently introduced as a hierarchical multiscale modeling tool for extending the length scales reached by stochastic simulations. Coarse-graining causes errors due to loss of degrees of freedom. To quantify these errors, theoretical error estimates derived using information loss theory are first presented. Simulations are subsequently carried out in the canonical ensemble for various combinations of key parameters suggested by theoretical estimates. Numerically evaluated errors are compared to theoretical error estimates to assess whether the latter can qualitatively capture the loss of information during coarse-graining. Finally, a standing wave example is presented to illustrate how these error estimates can be used to control accuracy in CG-KMC by employing adaptive meshes.

CITADO POR
  1. Vlachos D. G., Temporal coarse-graining of microscopic-lattice kinetic Monte Carlo simulations viaτleaping, Physical Review E, 78, 4, 2008. Crossref

  2. Chatterjee Abhijit, Vlachos Dionisios G., An overview of spatial microscopic and accelerated kinetic Monte Carlo methods, Journal of Computer-Aided Materials Design, 14, 2, 2007. Crossref

  3. Chatterjee Abhijit, Vlachos Dionisios G., Multiscale spatial Monte Carlo simulations: Multigriding, computational singular perturbation, and hierarchical stochastic closures, The Journal of Chemical Physics, 124, 6, 2006. Crossref

  4. Chatterjee Abhijit, Vlachos Dionisios G., Continuum mesoscopic framework for multiple interacting species and processes on multiple site types and/or crystallographic planes, The Journal of Chemical Physics, 127, 3, 2007. Crossref

  5. Chatterjee Abhijit, Vlachos Dionisios G., Temporal acceleration of spatially distributed kinetic Monte Carlo simulations, Journal of Computational Physics, 211, 2, 2006. Crossref

  6. Martínez E., Monasterio P.R., Marian J., Billion-atom synchronous parallel kinetic Monte Carlo simulations of critical 3D Ising systems, Journal of Computational Physics, 230, 4, 2011. Crossref

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