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Learning with Graph Kernels in the Chemical Universe

Tang, Yu-Hang


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        <foaf:name>Tang, Yu-Hang</foaf:name>
        <foaf:givenName>Yu-Hang</foaf:givenName>
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            <foaf:name>Lawrence Berkeley National Laboratory</foaf:name>
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    <dct:title>Learning with Graph Kernels in the Chemical Universe</dct:title>
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    <dct:issued rdf:datatype="http://www.w3.org/2001/XMLSchema#gYear">2019</dct:issued>
    <dcat:keyword>machine learning</dcat:keyword>
    <dcat:keyword>graph</dcat:keyword>
    <dcat:keyword>active learning</dcat:keyword>
    <dcat:keyword>molecular prediction</dcat:keyword>
    <dcat:keyword>computational chemistry</dcat:keyword>
    <dcat:keyword>kernel</dcat:keyword>
    <dcat:keyword>similarity</dcat:keyword>
    <dct:issued rdf:datatype="http://www.w3.org/2001/XMLSchema#date">2019-08-08</dct:issued>
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    <dct:description>&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;during the 2019 &lt;a href="https://data-science.llnl.gov/latest/workshop-2019#Day%202"&gt;DSI Data Science Workshop&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;</dct:description>
    <dct:description xml:lang="">{"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"]}</dct:description>
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