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dc.contributor.authorShi, Jiale
dc.contributor.authorWalsh, Dylan
dc.contributor.authorZou, Weizhong
dc.contributor.authorRebello, Nathan J
dc.contributor.authorDeagen, Michael E
dc.contributor.authorFransen, Katharina A
dc.contributor.authorGao, Xian
dc.contributor.authorOlsen, Bradley D
dc.contributor.authorAudus, Debra J
dc.date.accessioned2025-11-14T21:11:02Z
dc.date.available2025-11-14T21:11:02Z
dc.date.issued2024-02-14
dc.identifier.urihttps://hdl.handle.net/1721.1/163668
dc.description.abstractSynthetic polymers, in contrast to small molecules and deterministic biomacromolecules, are typically ensembles composed of polymer chains with varying numbers, lengths, sequences, chemistry, and topologies. While numerous approaches exist for measuring pairwise similarity among small molecules and sequence-defined biomacromolecules, accurately determining the pairwise similarity between two polymer ensembles remains challenging. This work proposes the earth mover's distance (EMD) metric to calculate the pairwise similarity score between two polymer ensembles. EMD offers a greater resolution of chemical differences between polymer ensembles than the averaging method and provides a quantitative numeric value representing the pairwise similarity between polymer ensembles in alignment with chemical intuition. The EMD approach for assessing polymer similarity enhances the development of accurate chemical search algorithms within polymer databases and can improve machine learning techniques for polymer design, optimization, and property prediction.en_US
dc.language.isoen
dc.publisherAmerican Chemical Societyen_US
dc.relation.isversionof10.1021/acspolymersau.3c00029en_US
dc.rightsCreative Commons Attribution-NonCommercial-NoDerivativesen_US
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/en_US
dc.sourceAmerican Chemical Societyen_US
dc.titleCalculating Pairwise Similarity of Polymer Ensembles via Earth Mover’s Distanceen_US
dc.typeArticleen_US
dc.identifier.citationJiale Shi, Dylan Walsh, Weizhong Zou, Nathan J. Rebello, Michael E. Deagen, Katharina A. Fransen, Xian Gao, Bradley D. Olsen, and Debra J. Audus. ACS Polymers Au 2024 4 (1), 66-76.en_US
dc.contributor.departmentMassachusetts Institute of Technology. Department of Chemical Engineeringen_US
dc.relation.journalACS Polymers Auen_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.updated2025-11-14T20:56:45Z
dspace.orderedauthorsShi, J; Walsh, D; Zou, W; Rebello, NJ; Deagen, ME; Fransen, KA; Gao, X; Olsen, BD; Audus, DJen_US
dspace.date.submission2025-11-14T20:56:46Z
mit.journal.volume4en_US
mit.journal.issue1en_US
mit.licensePUBLISHER_CC
mit.metadata.statusAuthority Work and Publication Information Neededen_US


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