Machine learning approach for relating conjugated polymer conformation to location of the exciton.

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dc.contributorCheung, Margaret S.
dc.contributorSimine, Lena
dc.contributorAllen, Thomas
dc.contributorRossky, Peter
dc.contributor.authorDuru, Ikenna
dc.date.accessioned2018-02-27T15:51:47Z
dc.date.available2018-02-27T15:51:47Z
dc.date.issued2017-10-12
dc.description.abstractThis project was completed with contributions from Lena Simine, Thomas Allen, and Peter Rossky from the Center for Theoretical Biological Physics, Rice University.
dc.description.departmentPhysics, Department of
dc.description.departmentHonors College
dc.identifier.urihttp://hdl.handle.net/10657/2464
dc.language.isoen_US
dc.rightsThe author of this work is the copyright owner. UH Libraries and the Texas Digital Library have their permission to store and provide access to this work. Further transmission, reproduction, or presentation of this work is prohibited except with permission of the author(s).
dc.titleMachine learning approach for relating conjugated polymer conformation to location of the exciton.
dc.typePoster

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