In silico veritas

dc.contributor.authorBarnard, Amanda S.en
dc.date.accessioned2026-01-01T11:41:00Z
dc.date.available2026-01-01T11:41:00Z
dc.date.issued2014-07-22en
dc.description.abstractInnovations in computational nanoscience have traditionally come in conjunction with experimental innovations, but uncertainty often surrounds the trustworthiness of in silico studies. While the accuracy of simulations has been improving every year, considerably less attention has focused on dealing with increasing complexity, which may be the source of concern. Creating more realistic virtual experiments (without sacrificing theoretical and numerical accuracy) remains challenging, particularly when we are confronted with the polydispersivity characteristic of extra silico samples. Fortunately, there are various theoretical methods that can be used in conjunction with first-principles simulations, not the least of which are the statistical tools and techniques promised by the emerging fields of materials informatics and data-driven sciences.en
dc.description.statusPeer-revieweden
dc.format.extent6en
dc.identifier.issn1936-0851en
dc.identifier.otherPubMed:24897567en
dc.identifier.otherORCID:/0000-0002-4784-2382/work/162952535en
dc.identifier.scopus84904762646en
dc.identifier.urihttps://hdl.handle.net/1885/733800052
dc.language.isoenen
dc.sourceACS Nanoen
dc.titleIn silico veritasen
dc.typeJournal articleen
dspace.entity.typePublicationen
local.bibliographicCitation.lastpage6525en
local.bibliographicCitation.startpage6520en
local.contributor.affiliationBarnard, Amanda S.; CSIROen
local.identifier.citationvolume8en
local.identifier.doi10.1021/nn502808yen
local.identifier.puref04f5707-d2f4-4478-a5a7-3640221cadeden
local.identifier.urlhttps://www.scopus.com/pages/publications/84904762646en
local.type.statusPublisheden

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