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Meeting	MS&T21: Materials Science & Technology
Symposium	Accelerating Materials Science with Big Data and Machine Learning
Presentation Title	Materials Graph Ontology for Improving the Standardization and Utilization of Materials Data
Author(s)	Sven Voigt, Surya Kalidindi
On-Site Speaker (Planned)	Sven Voigt
Abstract Scope	To maximize the utility of the materials data being generated in large volumes, it is necessary to store the data such that it is findable, accessible, interoperable, and reusable (FAIR). Although current materials data repositories, databases, and ontologies partly address FAIR principles, they do not adequately capture the critical metadata on contextual information (e.g., relationships between materials data and terms used by materials scientists such as process, structure, and property). This work develops the materials graph and materials graph ontology to standardize, collect, and organize the relational metadata that allows advanced queries that implicitly improve FAIR characteristics and utilization of the data. Case studies show the proposed materials graph ontology flexibly describes a broad variety of materials data, improves the findability and utility of the different graph-connected material concepts and data, and formalizes a materials data ingest framework that is amenable for extracting process-structure–property relationships.

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