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dc.contributor.authorGuo, Minghao
dc.contributor.authorShou, Wan
dc.contributor.authorMakatura, Liane
dc.contributor.authorErps, Timothy
dc.contributor.authorFoshey, Michael
dc.contributor.authorMatusik, Wojciech
dc.date.accessioned2022-07-18T13:56:02Z
dc.date.available2022-07-18T13:56:02Z
dc.date.issued2022-06-09
dc.identifier.urihttps://hdl.handle.net/1721.1/143799
dc.description.abstractPolymers are widely studied materials with diverse properties and applications determined by molecular structures. It is essential to represent these structures clearly and explore the full space of achievable chemical designs. However, existing approaches cannot offer comprehensive design models for polymers because of their inherent scale and structural complexity. Here, a parametric, context-sensitive grammar designed specifically for polymers (PolyGrammar) is proposed. Using the symbolic hypergraph representation and 14 simple production rules, PolyGrammar can represent and generate all valid polyurethane structures. An algorithm is presented to translate any polyurethane structure from the popular Simplified Molecular-Input Line-entry System (SMILES) string format into the PolyGrammar representation. The representative power of PolyGrammar is tested by translating a dataset of over 600 polyurethane samples collected from the literature. Furthermore, it is shown that PolyGrammar can be easily extended to other copolymers and homopolymers. By offering a complete, explicit representation scheme and an explainable generative model with validity guarantees, PolyGrammar takes an essential step toward a more comprehensive and practical system for polymer discovery and exploration. As the first bridge between formal languages and chemistry, PolyGrammar also serves as a critical blueprint to inform the design of similar grammars for other chemistries, including organic and inorganic molecules.en_US
dc.language.isoen
dc.publisherWileyen_US
dc.relation.isversionof10.1002/advs.202101864en_US
dc.rightsCreative Commons Attribution 4.0 International licenseen_US
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/en_US
dc.sourceWileyen_US
dc.titlePolygrammar: Grammar for Digital Polymer Representation and Generationen_US
dc.typeArticleen_US
dc.identifier.citationGuo, Minghao, Shou, Wan, Makatura, Liane, Erps, Timothy, Foshey, Michael et al. 2022. "Polygrammar: Grammar for Digital Polymer Representation and Generation." Advanced Science.
dc.contributor.departmentMassachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
dc.relation.journalAdvanced Scienceen_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.updated2022-07-18T13:47:03Z
dspace.orderedauthorsGuo, M; Shou, W; Makatura, L; Erps, T; Foshey, M; Matusik, Wen_US
dspace.date.submission2022-07-18T13:47:07Z
mit.licensePUBLISHER_CC
mit.metadata.statusAuthority Work and Publication Information Neededen_US


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