NanoMine schema: An extensible data representation for polymer nanocomposites

He Zhao, Yixing Wang, Anqi Lin, Bingyin Hu, Rui Yan, James McCusker, Wei Chen, Deborah L. McGuinness, Linda Schadler, L. Catherine Brinson*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

14 Scopus citations

Abstract

Polymer nanocomposites consist of a polymer matrix and fillers with at least one dimension below 100 nanometers (nm) [L. Schadler et al., Jom 59(3), 53-60 (2007)]. A key challenge in constructing an effective data resource for polymer nanocomposites is building a consistent, coherent, and clear data representation of all relevant parameters and their interrelationships. The data resource must address (1) data representation for representing, saving, and accessing the data (e.g., a data schema used in a data resource such as a database management system), (2) data contribution and uploading (e.g., an MS Excel template file that users can use to input data), (3) concept and knowledge modeling in a computationally accessible form (e.g., generation of a knowledge graph and ontology), and (4) ultimately data analytics and mining for new materials discovery. This paper addresses the first three issues, paving the way for rich, nuanced data analysis. We present the NanoMine polymer nanocomposite schema as an XML-based data schema designed for nanocomposite materials data representation and distribution and discuss its relationship to a higher level polymer data core consistent with other centralized materials data efforts. We also demonstrate aspects of data entry in an accessible manner consistent with the XML schema and discuss our mapping and augmentation approach to provide a more comprehensive representation in the form of an ontology and an ontology-enabled knowledge graph framework for nanopolymer systems. The schema and ontology and their easy accessibility and compatibility with parallel material standards provide a platform for data storage and search, customized visualization, and machine learning tools for material discovery and design.

Original languageEnglish (US)
Article number111108
JournalAPL Materials
Volume6
Issue number11
DOIs
StatePublished - Nov 1 2018

ASJC Scopus subject areas

  • Materials Science(all)
  • Engineering(all)

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