Presentation Open Access
<?xml version='1.0' encoding='utf-8'?> <resource xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xmlns="http://datacite.org/schema/kernel-4" xsi:schemaLocation="http://datacite.org/schema/kernel-4 http://schema.datacite.org/meta/kernel-4.1/metadata.xsd"> <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"><p>Presentations slides of <a href="https://crd.lbl.gov/departments/computational-science/ccmc/staff/alvarez-fellows/yu-hang-tang/">Yu-Hang Tang</a>&nbsp;on application of active machine learning and graph kernels. The talk also features the release of the <a href="https://pypi.org/project/graphdot/">GraphDot</a> library.</p></description> <description descriptionType="Other">{"references": ["Tang, Y. H., & 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> </resource>
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