Presentation Open Access

Learning with Graph Kernels in the Chemical Universe

Tang, Yu-Hang


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  <identifier identifierType="DOI">10.5281/zenodo.3433276</identifier>
  <creators>
    <creator>
      <creatorName>Tang, Yu-Hang</creatorName>
      <givenName>Yu-Hang</givenName>
      <familyName>Tang</familyName>
      <nameIdentifier nameIdentifierScheme="ORCID" schemeURI="http://orcid.org/">0000-0001-7424-5439</nameIdentifier>
      <affiliation>Lawrence Berkeley National Laboratory</affiliation>
    </creator>
  </creators>
  <titles>
    <title>Learning with Graph Kernels in the Chemical Universe</title>
  </titles>
  <publisher>Zenodo</publisher>
  <publicationYear>2019</publicationYear>
  <subjects>
    <subject>machine learning</subject>
    <subject>graph</subject>
    <subject>active learning</subject>
    <subject>molecular prediction</subject>
    <subject>computational chemistry</subject>
    <subject>kernel</subject>
    <subject>similarity</subject>
  </subjects>
  <dates>
    <date dateType="Issued">2019-08-08</date>
  </dates>
  <resourceType resourceTypeGeneral="Text">Presentation</resourceType>
  <alternateIdentifiers>
    <alternateIdentifier alternateIdentifierType="url">https://zenodo.org/record/3433276</alternateIdentifier>
  </alternateIdentifiers>
  <relatedIdentifiers>
    <relatedIdentifier relatedIdentifierType="DOI" relationType="IsVersionOf">10.5281/zenodo.3364077</relatedIdentifier>
  </relatedIdentifiers>
  <rightsList>
    <rights rightsURI="https://creativecommons.org/licenses/by-nc-nd/4.0/legalcode">Creative Commons Attribution Non Commercial No Derivatives 4.0 International</rights>
    <rights rightsURI="info:eu-repo/semantics/openAccess">Open Access</rights>
  </rightsList>
  <descriptions>
    <description descriptionType="Abstract">&lt;p&gt;Presentations slides of &lt;a href="https://crd.lbl.gov/departments/computational-science/ccmc/staff/alvarez-fellows/yu-hang-tang/"&gt;Yu-Hang Tang&lt;/a&gt;&amp;nbsp;on application of active machine learning and graph kernels. The talk also features the release of the &lt;a href="https://pypi.org/project/graphdot/"&gt;GraphDot&lt;/a&gt; library.&lt;/p&gt;</description>
    <description descriptionType="Other">{"references": ["Tang, Y. H., &amp; de Jong, W. A. (2019). Prediction of atomization energy using graph kernel and active learning. The Journal of chemical physics, 150(4), 044107. https://doi.org/10.1063/1.5078640"]}</description>
  </descriptions>
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