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dc.contributor.authorHong, Wesley Terrence
dc.contributor.authorWelsch, Roy E
dc.contributor.authorShao-Horn, Yang
dc.date.accessioned2017-06-06T15:12:09Z
dc.date.available2017-06-06T15:12:09Z
dc.date.issued2015-12
dc.date.submitted2015-10
dc.identifier.issn1932-7447
dc.identifier.issn1932-7455
dc.identifier.urihttp://hdl.handle.net/1721.1/109612
dc.description.abstractCatalysts for oxygen electrochemical processes are critical for the commercial viability of renewable energy storage and conversion devices such as fuel cells, artificial photosynthesis, and metal-air batteries. Transition metal oxides are an excellent system for developing scalable, non-noble-metal-based catalysts, especially for the oxygen evolution reaction (OER). Central to the rational design of novel catalysts is the development of quantitative structure–activity relationships, which correlate the desired catalytic behavior to structural and/or elemental descriptors of materials. The ultimate goal is to use these relationships to guide materials design. In this study, 101 intrinsic OER activities of 51 perovskites were compiled from five studies in literature and additional measurements made for this work. We explored the behavior and performance of 14 descriptors of the metal–oxygen bond strength using a number of statistical approaches, including factor analysis and linear regression models. We found that these descriptors can be classified into five descriptor families and identify electron occupancy and metal–oxygen covalency as the dominant influences on the OER activity. However, multiple descriptors still need to be considered in order to develop strong predictive relationships, largely outperforming the use of only one or two descriptors (as conventionally done in the field). We confirmed that the number of d electrons, charge-transfer energy (covalency), and optimality of eg occupancy play the important roles, but found that structural factors such as M–O–M bond angle and tolerance factor are relevant as well. With these tools, we demonstrate how statistical learning can be used to draw novel physical insights and combined with data mining to rapidly screen OER electrocatalysts across a wide chemical space.en_US
dc.description.sponsorshipUnited States. Department of Energy (SISGR DE-SC0002633)en_US
dc.description.sponsorshipSkoltech-MIT Center for Electrochemical Energy Storageen_US
dc.language.isoen_US
dc.publisherAmerican Chemical Society (ACS)en_US
dc.relation.isversionofhttp://dx.doi.org/10.1021/acs.jpcc.5b10071en_US
dc.rightsArticle is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.en_US
dc.sourceProf. Shao-Horn via Angie Locknaren_US
dc.titleDescriptors of Oxygen-Evolution Activity for Oxides: A Statistical Evaluationen_US
dc.typeArticleen_US
dc.identifier.citationHong, Wesley T., Roy E. Welsch, and Yang Shao-Horn. “Descriptors of Oxygen-Evolution Activity for Oxides: A Statistical Evaluation.” The Journal of Physical Chemistry C 120, no. 1 (January 14, 2016): 78–86.en_US
dc.contributor.departmentMassachusetts Institute of Technology. Department of Materials Science and Engineeringen_US
dc.contributor.departmentMassachusetts Institute of Technology. Department of Mechanical Engineeringen_US
dc.contributor.departmentSloan School of Managementen_US
dc.contributor.approverShao-Horn, Yangen_US
dc.contributor.mitauthorHong, Wesley Terrence
dc.contributor.mitauthorWelsch, Roy E
dc.contributor.mitauthorShao-Horn, Yang
dc.relation.journalThe Journal of Physical Chemistry Cen_US
dc.eprint.versionAuthor's final manuscripten_US
dc.type.urihttp://purl.org/eprint/type/JournalArticleen_US
eprint.statushttp://purl.org/eprint/status/PeerRevieweden_US
dspace.orderedauthorsHong, Wesley T.; Welsch, Roy E.; Shao-Horn, Yangen_US
dspace.embargo.termsNen_US
dc.identifier.orcidhttps://orcid.org/0000-0003-1560-0749
dc.identifier.orcidhttps://orcid.org/0000-0002-9038-1622
mit.licensePUBLISHER_POLICYen_US
mit.metadata.statusComplete


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