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Machine learning for analysis of experimental scattering and spectroscopy data in materials chemistry.

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Title: Machine learning for analysis of experimental scattering and spectroscopy data in materials chemistry.
Authors: Anker AS; Department of Chemistry and Nano-Science Center, University of Copenhagen 2100 Copenhagen Ø Denmark kirsten@chem.ku.dk., Butler KT; Department of Chemistry, University College London Gower Street London WC1E 6BT UK., Selvan R; Department of Computer Science, University of Copenhagen 2100 Copenhagen Ø Denmark.; Department of Neuroscience, University of Copenhagen 2200 Copenhagen N Denmark., Jensen KMØ; Department of Chemistry and Nano-Science Center, University of Copenhagen 2100 Copenhagen Ø Denmark kirsten@chem.ku.dk.
Source: Chemical science [Chem Sci] 2023 Nov 22; Vol. 14 (48), pp. 14003-14019. Date of Electronic Publication: 2023 Nov 22 (Print Publication: 2023).
Publication Type: Journal Article; Review
Journal Info: Publisher: Royal Society of Chemistry Country of Publication: England NLM ID: 101545951 Publication Model: eCollection Cited Medium: Print ISSN: 2041-6520 (Print) Linking ISSN: 20416520 NLM ISO Abbreviation: Chem Sci Subsets: PubMed not MEDLINE
Database: MEDLINE Ultimate
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  Data: Machine learning for analysis of experimental scattering and spectroscopy data in materials chemistry.
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  Data: <searchLink fieldCode="AU" term="%22Anker+AS%22">Anker AS</searchLink>; Department of Chemistry and Nano-Science Center, University of Copenhagen 2100 Copenhagen Ø Denmark kirsten@chem.ku.dk.<br /><searchLink fieldCode="AU" term="%22Butler+KT%22">Butler KT</searchLink>; Department of Chemistry, University College London Gower Street London WC1E 6BT UK.<br /><searchLink fieldCode="AU" term="%22Selvan+R%22">Selvan R</searchLink>; Department of Computer Science, University of Copenhagen 2100 Copenhagen Ø Denmark.; Department of Neuroscience, University of Copenhagen 2200 Copenhagen N Denmark.<br /><searchLink fieldCode="AU" term="%22Jensen+KMØ%22">Jensen KMØ</searchLink>; Department of Chemistry and Nano-Science Center, University of Copenhagen 2100 Copenhagen Ø Denmark kirsten@chem.ku.dk.
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  Data: <searchLink fieldCode="JN" term="%22101545951%22">Chemical science</searchLink> [Chem Sci] 2023 Nov 22; Vol. 14 (48), pp. 14003-14019. <i>Date of Electronic Publication: </i>2023 Nov 22 (<i>Print Publication: </i>2023).
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  Data: <i>Publisher: </i><searchLink fieldCode="PB" term="%22Royal+Society+of+Chemistry%22">Royal Society of Chemistry </searchLink><i>Country of Publication: </i>England <i>NLM ID: </i>101545951 <i>Publication Model: </i>eCollection <i>Cited Medium: </i>Print <i>ISSN: </i>2041-6520 (Print) <i>Linking ISSN: </i><searchLink fieldCode="IS" term="%2220416520%22">20416520 </searchLink><i>NLM ISO Abbreviation: </i>Chem Sci <i>Subsets: </i>PubMed not MEDLINE
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=mdl&AN=38098730
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        Value: 10.1039/d3sc05081e
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      – Code: eng
        Text: English
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        StartPage: 14003
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      – TitleFull: Machine learning for analysis of experimental scattering and spectroscopy data in materials chemistry.
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              Text: 2023 Nov 22
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              Y: 2023
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