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dc.contributor.authorGrossfield, Alan
dc.contributor.authorPatrone, Paul N.
dc.contributor.authorRoe, Daniel R.
dc.contributor.authorSchultz, Andrew J.
dc.contributor.authorSiderius, Daniel W.
dc.contributor.authorZuckerman, Daniel M.
dc.date.accessioned2019-09-24T19:28:44Z
dc.date.available2019-09-24T19:28:44Z
dc.date.issued2019
dc.identifier.citationGrossfield. A.; Patrone, P.N.; Roe, D.R.; Schultz, A.J.; Siderius, D.W. and Zuckerman. D.M. (2019) Best Practices for Quantification of Uncertainty and Sampling Quality in Molecular Simulations [Article v1.0]. Living Journal of Computational Molecular Science, 1(1), 5067, 24pp. DOI: https://doi.org/10.33011/livecoms.1.1.5067en_US
dc.identifier.issn2575-6524
dc.identifier.urihttp://hdl.handle.net/11329/1048
dc.identifier.urihttp://dx.doi.org/10.25607/OBP-576
dc.description.abstractThe quantitative assessment of uncertainty and sampling quality is essential in molecular simulation. Many systems of interest are highly complex, often at the edge of current computational capabilities. Modelers must therefore analyze and communicate statistical uncertainties so that “consumers” of simulated data understand its significance and limitations. This article covers key analyses appropriate for trajectory data generated by conventional simulation methods such as molecular dynamics and (single Markov chain) Monte Carlo. It also provides guidance for analyzing some ‘enhanced’ sampling approaches. We do not discuss systematic errors arising, e.g., from inaccuracy in the chosen model or force field.en_US
dc.language.isoenen_US
dc.subject.otherStatistical uncertaintiesen_US
dc.subject.otherUncertainty quantificationen_US
dc.subject.otherModel uncertaintyen_US
dc.titleBest Practices for Quantification of Uncertainty and Sampling Quality in Molecular Simulations [Article v1.0].en_US
dc.typeJournal Contributionen_US
dc.description.refereedRefereeden_US
dc.format.pagerange24pp.en_US
dc.identifier.doihttps://doi.org/10.33011/livecoms.1.1.5067
dc.subject.parameterDisciplineParameter Discipline::Cross-disciplineen_US
dc.subject.dmProcessesData Management Practices::Data analysisen_US
dc.bibliographicCitation.titleLiving Journal of Computational Molecular Scienceen_US
dc.bibliographicCitation.volume1en_US
dc.bibliographicCitation.issue1, 5067en_US
dc.description.bptypeBest Practiceen_US
dc.description.bptypeGuideen_US
obps.contact.contactnameAlan Grossfield
obps.contact.contactemailalan_grossfield@urmc.rochester.edu
obps.resourceurl.publisherhttps://www.livecomsjournal.org/article/5067-best-practices-for-quantification-of-uncertainty-and-sampling-quality-in-molecular-simulations-article-v1-0en_US
obps.resourceurl.publisherhttps://github.com/dmzuckerman/Sampling-Uncertainty


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