{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,22]],"date-time":"2026-09-22T16:14:02Z","timestamp":1790093642648,"version":"4.0.1"},"reference-count":50,"publisher":"American Chemical Society (ACS)","issue":"37","license":[{"start":{"date-parts":[[2026,9,4]],"date-time":"2026-09-04T00:00:00Z","timestamp":1788480000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100000287","name":"Royal Academy of Engineering","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100000287","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100004349","name":"BASF","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100004349","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026,9,22]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Polymer informatics has emerged as an area of interest in the search for more sustainable plastics. Yet, there is a dearth in large, high-quality, polymer-based materials databases that are open-source for the polymer research community. Efforts to curate such databases involve autogenerating large experimental materials databases by mining chemical data from scientific literature, with a specific focus on mining tabular data as these are particularly rich sources of polymeric information. This study compares the performance of two tools in extracting large volumes of tabular data about polymer names and their glass-transition, melting and decomposition temperatures: a table-extraction tool that employs a downstream neural network to resolve polymer names from the table fields, and the table-mining part of the \u201cchemistry-aware\u201d natural-language-processing tool, ChemDataExtractor. We find that both methods afford high precision. The recall of the former is boosted by preserving some of the implicit structure of the source tables, while the latter offers a far wider scope of knowledge extraction from the literature on polymer science.<\/jats:p>","DOI":"10.1021\/acsomega.6c05295","type":"journal-article","created":{"date-parts":[[2026,9,4]],"date-time":"2026-09-04T14:11:32Z","timestamp":1788531092000},"page":"55797-55805","source":"Crossref","is-referenced-by-count":0,"title":["Table-Based Text\nParsing Methods for Polymer Science:\nA Comparative Study"],"prefix":"10.1021","volume":"11","author":[{"given":"Rian J.","family":"Howe","sequence":"first","affiliation":[{"name":"Cavendish Laboratory, Department of Physics , , J.J. Thomson Avenue , ,","place":["Cambridge, U.K., CB3 0US"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1552-8743","authenticated-orcid":true,"given":"Jacqueline M.","family":"Cole","sequence":"additional","affiliation":[{"name":"Cavendish Laboratory, Department of Physics , , J.J. Thomson Avenue , ,","place":["Cambridge, U.K., CB3 0US"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"316","published-online":{"date-parts":[[2026,9,4]]},"reference":[{"key":"2026092210563315900_cit1","unstructured":"United Nations Environment Program.\n          \n          Gameplan: It\u2019s\ntime to beat plastic polution; https:\/\/www.unep.org\/beatpollution\/beat-plastic-pollution\/gameplan-it-is-time-to-beat-plastic-pollution, (accessed: 11\/4\/2024)."},{"key":"2026092210563315900_cit2","unstructured":"United Nations Environment Program.\n          \n          Turning off\nthe Tap: How the world can end plastic pollution and create a circular\neconomy. https:\/\/www.un-ilibrary.org\/content\/books\/9789280740240, 2023; (accessed: 11\/4\/2024)."},{"key":"2026092210563315900_cit3","unstructured":"European Commision.\n          \n          Plastic waste and recycling\nin the EU: facts and figures. https:\/\/www.europarl.europa.eu\/topics\/en\/article\/20181212STO21610\/plastic-waste-and-recycling-in-the-eu-facts-and-figures, (accessed: 30\/3\/2024)."},{"key":"2026092210563315900_cit4","doi-asserted-by":"publisher","first-page":"4684","DOI":"10.1021\/acs.jcim.0c00726","article-title":"PI1M: A Benchmark Database\nfor Polymer Informatics","volume":"60","author":"Ma","year":"2020","journal-title":"J. Chem. Inf. Model."},{"key":"2026092210563315900_cit5","doi-asserted-by":"publisher","first-page":"4099","DOI":"10.1038\/s41467-023-39868-6","article-title":"polyBERT:\nA chemical language model to enable fully\nmachine-driven ultrafast polymer informatics","volume":"14","author":"Kuenneth","year":"2023","journal-title":"Nat. Commun."},{"key":"2026092210563315900_cit6","doi-asserted-by":"publisher","first-page":"245","DOI":"10.1038\/s43588-025-00768-y","article-title":"Harnessing\nlarge language models for data-scarce learning of polymer\nproperties","volume":"5","author":"Liu","year":"2025","journal-title":"Nat. Comput. Sci."},{"key":"2026092210563315900_cit7","doi-asserted-by":"publisher","first-page":"011002","DOI":"10.1063\/1.4812323","article-title":"Commentary:\nThe Materials Project: A materials genome\napproach to accelerating materials innovation","volume":"1","author":"Jain","year":"2013","journal-title":"APL Mater."},{"key":"2026092210563315900_cit8","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.2510.19774","article-title":"How Accurate Are\nDFT Forces? Unexpectedly Large Uncertainties in Molecular Datasets","author":"Kuryla","year":"2025","journal-title":"arXiv"},{"key":"2026092210563315900_cit9","doi-asserted-by":"publisher","first-page":"100595","DOI":"10.1016\/j.mser.2020.100595","article-title":"Polymer informatics: Current status\nand critical next steps","volume":"144","author":"Chen","year":"2021","journal-title":"Mater. Sci. Eng., R"},{"key":"2026092210563315900_cit10","doi-asserted-by":"publisher","first-page":"8661","DOI":"10.1021\/acs.macromol.4c01410","article-title":"Automation and Machine Learning for\nAccelerated Polymer Characterization and Development: Past, Potential,\nand a Path Forward","volume":"57","author":"Beaucage","year":"2024","journal-title":"Macromolecules"},{"key":"2026092210563315900_cit11","doi-asserted-by":"publisher","first-page":"239","DOI":"10.1021\/acspolymersau.2c00053","article-title":"Emerging Trends\nin Machine Learning: A Polymer Perspective","volume":"3","author":"Martin","year":"2023","journal-title":"ACS Polym. Au"},{"key":"2026092210563315900_cit12","doi-asserted-by":"publisher","first-page":"752","DOI":"10.1557\/s43579-024-00587-8","article-title":"A prospective on machine learning\nchallenges, progress, and potential in polymer science","volume":"14","author":"Struble","year":"2024","journal-title":"MRS Commun."},{"key":"2026092210563315900_cit13","doi-asserted-by":"publisher","first-page":"1418","DOI":"10.1038\/s41467-024-45563-x","article-title":"Structured\ninformation\nextraction from scientific text with large language models","volume":"15","author":"Dagdelen","year":"2024","journal-title":"Nat. Commun."},{"key":"2026092210563315900_cit14","doi-asserted-by":"crossref","first-page":"22","DOI":"10.1109\/EIDWT.2011.13","article-title":"PoLyInfo:\nPolymer database\nfor polymeric materials design","volume-title":"2011 International\nConference On Emerging Intelligent Data And Web Technologies","author":"Otsuka","year":"2011"},{"key":"2026092210563315900_cit15","doi-asserted-by":"publisher","first-page":"363","DOI":"10.1002\/mats.1994.040030207","article-title":"Neural networks and graph theory as computational tools\nfor predicting polymer properties","volume":"3","author":"Sumpter","year":"1994","journal-title":"Macromol.\nTheory Simul."},{"key":"2026092210563315900_cit16","doi-asserted-by":"publisher","first-page":"5957","DOI":"10.1021\/acs.macromol.1c00728","article-title":"Copolymer Informatics with Multitask\nDeep Neural Networks","volume":"54","author":"Kuenneth","year":"2021","journal-title":"Macromolecules"},{"key":"2026092210563315900_cit17","doi-asserted-by":"publisher","first-page":"5838","DOI":"10.1016\/j.matpr.2021.02.730","article-title":"Deep learning based\napproach for prediction of glass transition temperature in polymers","volume":"46","author":"Goswami","year":"2021","journal-title":"Mater. Today: Proc."},{"key":"2026092210563315900_cit18","doi-asserted-by":"publisher","DOI":"10.1016\/j.heliyon.2020.e05055","article-title":"Machine learning glass transition\ntemperature of polymers","volume":"6","author":"Zhang","year":"2020","journal-title":"Heliyon"},{"key":"2026092210563315900_cit19","doi-asserted-by":"publisher","first-page":"1049","DOI":"10.3390\/polym16081049","article-title":"Interpretable Machine\nLearning Framework to Predict the Glass Transition\nTemperature of Polymers","volume":"16","author":"Uddin","year":"2024","journal-title":"Polymers"},{"key":"2026092210563315900_cit20","doi-asserted-by":"publisher","first-page":"4449","DOI":"10.1021\/acsapm.3c03040","article-title":"Predicting the Glass\nTransition Temperature of Biopolymers via High-Throughput\nMolecular Dynamics Simulations and Machine Learning","volume":"6","author":"Mart\u0131","year":"2024","journal-title":"ACS Appl. Polym. Mater."},{"key":"2026092210563315900_cit21","doi-asserted-by":"publisher","first-page":"8547","DOI":"10.1021\/acs.macromol.3c00994","article-title":"PolyID: Artificial\nIntelligence for Discovering Performance-Advantaged and Sustainable\nPolymers","volume":"56","author":"Wilson","year":"2023","journal-title":"Macromolecules"},{"issue":"17","key":"2026092210563315900_cit22","doi-asserted-by":"publisher","first-page":"171104","DOI":"10.1063\/5.0023759","article-title":"Machine-learning predictions\nof polymer properties with Polymer Genome","volume":"128","author":"Doan Tran","year":"2020","journal-title":"J.\nAppl. Phys."},{"key":"2026092210563315900_cit23","doi-asserted-by":"publisher","first-page":"17575","DOI":"10.1021\/acs.jpcc.8b02913","article-title":"Polymer Genome:\nA Data-Powered Polymer Informatics Platform for Property Predictions","volume":"122","author":"Kim","year":"2018","journal-title":"J. Phys. Chem. 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Chem. Inf. Model."},{"key":"2026092210563315900_cit31","doi-asserted-by":"publisher","first-page":"4280","DOI":"10.1021\/acs.jcim.1c00446","article-title":"ChemDataExtractor\n2.0: Autopopulated Ontologies for Materials Science","volume":"61","author":"Mavrac\u030cic\u0301","year":"2021","journal-title":"J. Chem. Inf. Model."},{"key":"2026092210563315900_cit32","doi-asserted-by":"publisher","first-page":"1207","DOI":"10.1021\/acs.jcim.1c01199","article-title":"Single Model for Organic and Inorganic\nChemical Named\nEntity Recognition in ChemDataExtractor","volume":"62","author":"Isazawa","year":"2022","journal-title":"J.\nChem. Inf. Model."},{"issue":"1","key":"2026092210563315900_cit33","doi-asserted-by":"publisher","first-page":"651","DOI":"10.1038\/s41597-023-02511-6","article-title":"Automated Construction of a Photocatalysis\nDataset\nfor Water-Splitting Applications","volume":"10","author":"Isazawa","year":"2023","journal-title":"Sci. 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Data"},{"key":"2026092210563315900_cit37","doi-asserted-by":"publisher","first-page":"193","DOI":"10.1038\/s41597-022-01294-6","article-title":"Auto-generated database\nof semiconductor band gaps\nusing ChemDataExtractor","volume":"9","author":"Dong","year":"2022","journal-title":"Sci. Data"},{"key":"2026092210563315900_cit38","doi-asserted-by":"publisher","first-page":"329","DOI":"10.1038\/s41597-022-01355-w","article-title":"Perovskite- and Dye-Sensitized Solar-Cell\nDevice Databases\nAuto-generated Using ChemDataExtractor","volume":"9","author":"Beard","year":"2022","journal-title":"Sci.\nData"},{"key":"2026092210563315900_cit39","doi-asserted-by":"publisher","first-page":"192","DOI":"10.1038\/s41597-022-01295-5","article-title":"A database of refractive indices\nand dielectric constants\nauto-generated using ChemDataExtractor","volume":"9","author":"Zhao","year":"2022","journal-title":"Sci.\nData"},{"key":"2026092210563315900_cit40","doi-asserted-by":"crossref","first-page":"109","DOI":"10.1109\/eScience.2017.23","article-title":"Towards a\nhybrid human-computer scientific information extraction pipeline","volume-title":"2017 IEEE 13th International Conference on e-Science (e-Science)","author":"Tchoua","year":"2017"},{"key":"2026092210563315900_cit41","doi-asserted-by":"publisher","first-page":"1961","DOI":"10.1021\/acs.jcim.2c01259","article-title":"OpticalBERT and OpticalTable-SQA:\nText- and Table-Based Language Models for the Optical-Materials Domain","volume":"63","author":"Zhao","year":"2023","journal-title":"J. Chem. Inf. Model."},{"issue":"1","key":"2026092210563315900_cit42","doi-asserted-by":"publisher","first-page":"1273","DOI":"10.1038\/s41597-024-03979-6","article-title":"A Database of Stress-Strain\nProperties\nAuto-generated from the Scientific Literature using ChemDataExtractor","volume":"11","author":"Kumar","year":"2024","journal-title":"Sci. Data"},{"issue":"1","key":"2026092210563315900_cit43","doi-asserted-by":"publisher","first-page":"52","DOI":"10.1038\/s41524-023-01003-w","article-title":"A general-purpose\nmaterial property data extraction pipeline from large polymer corpora\nusing natural language processing","volume":"9","author":"Shetty","year":"2023","journal-title":"npj Comput.\nMater."},{"key":"2026092210563315900_cit44","doi-asserted-by":"publisher","first-page":"269","DOI":"10.1038\/s43246-024-00708-9","article-title":"Data extraction\nfrom polymer literature using large language models","volume":"5","author":"Gupta","year":"2024","journal-title":"Commun. Mater"},{"key":"2026092210563315900_cit45","doi-asserted-by":"publisher","first-page":"12","DOI":"10.1080\/27660400.2021.1899456","article-title":"Machine extraction\nof polymer data from tables using XML versions\nof scientific articles","volume":"1","author":"Oka","year":"2021","journal-title":"Sci. Technol. Adv. Mater.:\nMethods"},{"key":"2026092210563315900_cit46","doi-asserted-by":"publisher","first-page":"287","DOI":"10.1016\/j.apsusc.2015.01.170","article-title":"Fabrication of honeycomb-structured\npoly(ethylene glycol)-block-poly(lactic acid) porous films and biomedical\napplications for cell growth","volume":"332","author":"Yao","year":"2015","journal-title":"Appl. Surf. Sci."},{"key":"2026092210563315900_cit47","doi-asserted-by":"crossref","first-page":"3615","DOI":"10.18653\/v1\/D19-1371","article-title":"SciBERT:\nA Pretrained Language Model\nfor Scientific Text","volume-title":"Proceedings of the\n2019 Conference on Empirical Methods in Natural Language Processing\nand the 9th International Joint Conference on Natural Language Processing\n(EMNLP-IJCNLP)","author":"Beltagy","year":"2019"},{"key":"2026092210563315900_cit48","unstructured":"CROW Polymer Properties\nDatabase. https:\/\/www.polymerdatabase.com\/home.html, (accessed: 10\/02\/2022)."},{"key":"2026092210563315900_cit49","first-page":"1818","article-title":"Learning Character-level\nRepresentations for Part-of-Speech Tagging","volume-title":"Proceedings of the 31st International Conference on Machine Learning","author":"Santos","year":"2014"},{"key":"2026092210563315900_cit50","doi-asserted-by":"publisher","DOI":"10.6084\/m9.figshare.30826232","article-title":"Table-based\ntext parsing methods for polymer science: A comparative study","volume-title":"Figshare","author":"Howe","year":"2026"}],"container-title":["ACS Omega"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/pubs.acs.org\/doi\/pdf\/10.1021\/acsomega.6c05295","content-type":"application\/pdf","content-version":"vor","intended-application":"unspecified"},{"URL":"https:\/\/pubs.acs.org\/acsodf\/article-pdf\/11\/37\/55797\/68373042\/acsomega.6c05295.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/pubs.acs.org\/acsodf\/article-pdf\/11\/37\/55797\/68373042\/acsomega.6c05295.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,9,22]],"date-time":"2026-09-22T15:50:22Z","timestamp":1790092222000},"score":1,"resource":{"primary":{"URL":"https:\/\/pubs.acs.org\/acsodf\/article\/11\/37\/55797\/5420496\/Table-Based-Text-Parsing-Methods-for-Polymer"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,9,4]]},"references-count":50,"journal-issue":{"issue":"37","published-online":{"date-parts":[[2026,9,4]]},"published-print":{"date-parts":[[2026,9,22]]}},"URL":"https:\/\/doi.org\/10.1021\/acsomega.6c05295","relation":{},"ISSN":["2470-1343"],"issn-type":[{"value":"2470-1343","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,9,4]]}}}