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dc.contributor.authorKumar, Jatin N.
dc.contributor.authorLi, Qianxiao
dc.contributor.authorTang, Karen Y. T.
dc.contributor.authorBuonassisi, Anthony
dc.contributor.authorGonzalez-Oyarce, Anibal L.
dc.contributor.authorYe, Jun
dc.date.accessioned2021-03-30T21:02:47Z
dc.date.available2021-03-30T21:02:47Z
dc.date.issued2019-07
dc.date.submitted2019-01
dc.identifier.issn2057-3960
dc.identifier.urihttps://hdl.handle.net/1721.1/130296
dc.description.abstractInverse design is an outstanding challenge in disordered systems with multiple length scales such as polymers, particularly when designing polymers with desired phase behavior. Here we demonstrate high-accuracy tuning of poly(2-oxazoline) cloud point via machine learning. With a design space of four repeating units and a range of molecular masses, we achieve an accuracy of 4 °C root mean squared error (RMSE) in a temperature range of 24–90 °C, employing gradient boosting with decision trees. The RMSE is >3x better than linear and polynomial regression. We perform inverse design via particle-swarm optimization, predicting and synthesizing 17 polymers with constrained design at 4 target cloud points from 37 to 80 °C. Our approach challenges the status quo in polymer design with a machine learning algorithm, that is capable of fast and systematic discovery of new polymers.en_US
dc.language.isoen
dc.publisherSpringer Science and Business Mediaen_US
dc.relation.isversionofhttp://dx.doi.org/10.1038/s41524-019-0209-9en_US
dc.rightsCreative Commons Attribution 4.0 International licenseen_US
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/en_US
dc.sourceNatureen_US
dc.titleMachine learning enables polymer cloud-point engineering via inverse designen_US
dc.typeArticleen_US
dc.identifier.citationKumar, Jatin N. et al. "Machine learning enables polymer cloud-point engineering via inverse design." npj Computational Materials 5, 1 (July 2019): 73. © 2019 The Author(s)en_US
dc.contributor.departmentMassachusetts Institute of Technology. Department of Mechanical Engineeringen_US
dc.relation.journalnpj Computational Materialsen_US
dc.eprint.versionFinal published versionen_US
dc.type.urihttp://purl.org/eprint/type/JournalArticleen_US
eprint.statushttp://purl.org/eprint/status/PeerRevieweden_US
dc.date.updated2020-06-24T19:33:04Z
dspace.date.submission2020-06-24T19:33:06Z
mit.journal.volume5en_US
mit.journal.issue1en_US
mit.licensePUBLISHER_CC
mit.metadata.statusComplete


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