Uncertainty aware machine learning for bridging simulation and experiment in high throughput materials characterization.

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Title: Uncertainty aware machine learning for bridging simulation and experiment in high throughput materials characterization.
Authors: Chen J; Department of Mechanical Engineering, Virginia Tech, Blacksburg, VA, USA.; VT Made, Virginia Tech, Blacksburg, VA, USA.; Macromolecules Innovation Institute, Virginia Tech, Blacksburg, VA, USA., Long T; Department of Materials Science and Engineering, Johns Hopkins University, Baltimore, MD, USA., Wall M; Department of Materials Science and Engineering, Johns Hopkins University, Baltimore, MD, USA., Hufnagel T; Department of Materials Science and Engineering, Johns Hopkins University, Baltimore, MD, USA., Chen W; Department of Mechanical Engineering, Northwestern University, Evanston, IL, USA. weichen@northwestern.edu.
Source: Scientific reports [Sci Rep] 2026 May 06; Vol. 16 (1). Date of Electronic Publication: 2026 May 06.
Publication Type: Journal Article
Journal Info: Publisher: Nature Publishing Group Country of Publication: England NLM ID: 101563288 Publication Model: Electronic Cited Medium: Internet ISSN: 2045-2322 (Electronic) Linking ISSN: 20452322 NLM ISO Abbreviation: Sci Rep Subsets: MEDLINE; PubMed not MEDLINE
Database: MEDLINE Ultimate
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  Data: <searchLink fieldCode="AU" term="%22Chen+J%22">Chen J</searchLink>; Department of Mechanical Engineering, Virginia Tech, Blacksburg, VA, USA.; VT Made, Virginia Tech, Blacksburg, VA, USA.; Macromolecules Innovation Institute, Virginia Tech, Blacksburg, VA, USA.<br /><searchLink fieldCode="AU" term="%22Long+T%22">Long T</searchLink>; Department of Materials Science and Engineering, Johns Hopkins University, Baltimore, MD, USA.<br /><searchLink fieldCode="AU" term="%22Wall+M%22">Wall M</searchLink>; Department of Materials Science and Engineering, Johns Hopkins University, Baltimore, MD, USA.<br /><searchLink fieldCode="AU" term="%22Hufnagel+T%22">Hufnagel T</searchLink>; Department of Materials Science and Engineering, Johns Hopkins University, Baltimore, MD, USA.<br /><searchLink fieldCode="AU" term="%22Chen+W%22">Chen W</searchLink>; Department of Mechanical Engineering, Northwestern University, Evanston, IL, USA. weichen@northwestern.edu.
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  Data: <searchLink fieldCode="JN" term="%22101563288%22">Scientific reports</searchLink> [Sci Rep] 2026 May 06; Vol. 16 (1). <i>Date of Electronic Publication: </i>2026 May 06.
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  Data: <i>Publisher: </i><searchLink fieldCode="PB" term="%22Nature+Publishing+Group%22">Nature Publishing Group </searchLink><i>Country of Publication: </i>England <i>NLM ID: </i>101563288 <i>Publication Model: </i>Electronic <i>Cited Medium: </i>Internet <i>ISSN: </i>2045-2322 (Electronic) <i>Linking ISSN: </i><searchLink fieldCode="IS" term="%2220452322%22">20452322 </searchLink><i>NLM ISO Abbreviation: </i>Sci Rep <i>Subsets: </i>MEDLINE; PubMed not MEDLINE
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        Value: 10.1038/s41598-026-51212-8
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              Text: 2026 May 06
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