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There is now a CONTENT FREEZE for Mercury while we switch to a new platform. It began on Friday, March 10 at 6pm and will end on Wednesday, March 15 at noon. No new content can be created during this time, but all material in the system as of the beginning of the freeze will be migrated to the new platform, including users and groups. Functionally the new site is identical to the old one. webteam@gatech.edu
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THE SCHOOL OF MATERIALS SCIENCE AND ENGINEERING
GEORGIA INSTITUTE OF TECHNOLOGY
Under the provisions of the regulations for the degree
DOCTOR OF PHILOSOPHY
on Monday, April 25, 2022
11:00 AM
via
BlueJeans Video Conferencing
https://bluejeans.com/787872949/5450
will be held the
DISSERTATION PROPOSAL DEFENSE
for
Sven Voigt
"A Knowledge Graph for Materials Informatics"
Committee Members:
Prof. Surya Kalidindi, Advisor, CSE/ME/MSE
Prof. David McDowell, ME/MSE
Prof. Josh Kacher, MSE
Prof. Sham Navathe, CoC
Zach Trautt, Ph.D., National Institute of Standards and Technology (NIST)
Abstract:
A knowledge graph (KG) helps meet the findable, accessible, interoperable, and reusable (FAIR) principles of data by capturing the important conceptual information needed to understand data such that it can be reused. In particular, this work proposes the materials informatics KG for capturing process-structure-property (PSP) relationships between data that is currently stored in a fragmented manner in order to make it FAIR. The KG is composed of three new components: an ontological model designed to digitally capture PSP materials knowledge, digital object architecture for KGs, and specific artificial intelligence (AI) tools that infer additional PSP knowledge. In addition to existing materials ontological models, the new ontological model will make PSP relationships explicit. In addition to current KG technology, which represents textual information, the new KG incorporates emerging digital object architecture concepts to capture knowledge in data files, which may also possess many heterogeneous file formats. In addition to existing AI tools, which are built for general KGs or are built for fragmented data, new AI tools purposely built to take advantage of the combination of the graphical structure and the specific materials PSP data are used to aggregate existing knowledge and extract new knowledge. The proposal is on the development and the validation of this KG, where validation includes demonstrating the ability to capture sufficient PSP knowledge, aggregating PSP knowledge using AI tools, and inferring the likelihood of PSP characteristics for a new material specimen given existing knowledge. These new technologies enable capturing and inferring much more knowledge than other materials data solutions and are combined with powerful KG query (e.g., search) capabilities to make materials data much more FAIR.
