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Machine learning for heterogeneous catalyst design and discovery
dc.contributor.authorGoldsmith, Bryan R.
dc.contributor.authorEsterhuizen, Jacques
dc.contributor.authorLiu, Jin‐xun
dc.contributor.authorBartel, Christopher J.
dc.contributor.authorSutton, Christopher
dc.date.accessioned2018-07-13T15:46:17Z
dc.date.available2019-09-04T20:15:38Zen
dc.date.issued2018-07
dc.identifier.citationGoldsmith, Bryan R.; Esterhuizen, Jacques; Liu, Jin‐xun ; Bartel, Christopher J.; Sutton, Christopher (2018). "Machine learning for heterogeneous catalyst design and discovery." AIChE Journal 64(7): 2311-2323.
dc.identifier.issn0001-1541
dc.identifier.issn1547-5905
dc.identifier.urihttps://hdl.handle.net/2027.42/144583
dc.publisherJohn Wiley & Sons
dc.subject.othercompressed sensing
dc.subject.othermachine learning
dc.subject.otherheterogeneous catalysis
dc.subject.othercomputational catalysis
dc.subject.otherdata mining
dc.titleMachine learning for heterogeneous catalyst design and discovery
dc.typeArticleen_US
dc.rights.robotsIndexNoFollow
dc.subject.hlbsecondlevelChemical Engineering
dc.subject.hlbtoplevelEngineering
dc.subject.hlbtoplevelScience
dc.description.peerreviewedPeer Reviewed
dc.description.bitstreamurlhttps://deepblue.lib.umich.edu/bitstream/2027.42/144583/1/aic16198.pdf
dc.description.bitstreamurlhttps://deepblue.lib.umich.edu/bitstream/2027.42/144583/2/aic16198_am.pdf
dc.identifier.doi10.1002/aic.16198
dc.identifier.sourceAIChE Journal
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