How to represent materials knowledge? Ontology CSSI Element: Nanocomposites to Metamaterials: A Knowledge Graph Framework PI: L. C. Brinson,1 Co-PIs: C. Rudin,1 W. Chen,2 D. McGuinness,3 C. Daraio,4 L. Schadler5 Institutions: 1Duke University, 2Northwestern, 3Rensselaer Polytech. Inst., 4Caltech, 5Univ. Vermont Award #: OAC-1835677 NSF CSSI PI Meeting, Alexandria, VA, July 25-26, 2022 MaterialsMine enables materials discovery and design via an extensible digital toolkit for sharing and visualizing materials research data. Take a picture to download the full poster Nanocomposites to Metamaterials: A Knowledge Graph Framework ● Capture data “from the wild” and apply metadata that make datasets searchable and interoperable ● As of July 13th, 244 papers from the literature and 2568 total experimental samples ● MS Excel-based template contains all parameters within XML schema ● Custom-configured Excel templates facilitate entry of many samples ● Conversion tool maps data from Excel templates into corresponding fields in XML database ● Excel template and data uploader are extensible and have been shared with NIST for use in other data resources How to ingest data? Data Curation Nanomine Data Demonstrated the interpretation of the model by modifying the inputs and correlated the learning with physical understanding What can be learned from the data? NanoMine Data Case Study New insights about the interplay of interphase, volume fraction and dispersion, explaining tan d trend in experimental data within NanoMine Microstructure Characterization & Reconstruction (MCR) This work aims to produce an organized, searchable, extensible repository of data on polymer nanocomposites and structural meta- materials complete with a suite of analytical and predictive tools to help spur the discovery of new materials. Metadata include materials information, composition, processing, measured properties, microstructure, and data provenance. Data generation over a wide range of shapes & properties Data-driven metamaterials design MetaMine Data Case Study Machine learning structure-property models 88,000 2D unit cells 8,000 3D unit cells Deep learning latent shape space Intelligent Characterization Statistical Reconstruction Help users select the appropriate MCR technique and achieve microstructure information following the NanoMine schema NanoMine For Polymer Nanocomposites MetaMine For Metamaterials NLP-driven Data Curation Future work Improve the Ease of Use By the Community! Future work We are building a Natural Language Processing (NLP) pipeline with two tasks to achieve (semi-)automated curation of archival polymer nanocomposites data in journal papers. ● Preliminary work achieves a micro-averaged F1 score of 0.87 on Named Entity Recognition (NER) task. ● Collected a polymer composite corpus of 23k+ papers. ● Annotated 1896 figure captions for NER and Relation Extraction (RE) task. NLP-Driven Data Curation ● To identify samples and properties being measured to populate the structure of a Schema ● Automatic population of the schema with user verification Modification of the ontology / Knowledge Graph from a user friendly interface Materials-Information Twin Tetrahedra (MITT) The paradigm shift toward data-intensive scientific research relies on collaboration between numerous disciplines and stakeholders. ● MITT is the nexus joining materials and information science, adapted from the concept of a “digital twin” ● Provides stakeholders with a platform to contextualize, translate, and direct efforts for next generation materials science advances QR Code Here Glass transition temperature (Tg) is tunable by changing the chemistry of the interface between the nanoparticles and polymer matrix. A case study using NanoMine and the Polymerizer web tool to investigate the relationship of the shift in Tg and parameters such as nanoparticle volume fraction and surface energies showed: ● Machine Learning could be used to show a quantitative relationship between s