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Meeting MS&T24: Materials Science & Technology
Symposium Materials Informatics for Images and Multi-dimensional Datasets
Presentation Title Advancing AI-Driven Analysis of Synchrotron Data via FAIR Practices, Ontology and Knowledge Graphs
Author(s) Mohommad Redad Mehdi, Finley Holt, Weiqi Yue, Alexander C.H. Bradley, Balashanmuga Priyan Rajamohan, Erika I. Barcelos, Daniel Savage, Hemant Sharma, Matthew A. Willard, Frank Ernst, Pawan K. Tripathi, Roger H. French
On-Site Speaker (Planned) Mohommad Redad Mehdi
Abstract Scope In this work, we define ontologies which is a formal representation of key concepts, properties, and relationships in a particular domain, providing a shared vocabulary for both humans and AI models for the large scale synchrotron high energy X-ray diffraction (HEXRD) datasets. Leveraging this foundation, we integrate knowledge graphs to establish the connection between these concepts and actual synchrotron data. This approach results in structured knowledge representation conforming to FAIR (Findable, Accessible, Interoperable, Reusable) data standards. These ontologies are used as a precursor for knowledge graphs that are stored in our Janusgraph database. The resulting knowledge graph allows us to execute complex graph computations on the dataset, perform ML techniques like link prediction and graph learning, and even accelerate data-driven discoveries. The improved accessibility and contextual information of data will empower AI-driven analysis of massive-scale synchrotron data.

OTHER PAPERS PLANNED FOR THIS SYMPOSIUM

Advancing AI-Driven Analysis of Synchrotron Data via FAIR Practices, Ontology and Knowledge Graphs
Advancing Sustainable Agriculture through Multiscale Spatiotemporal Data Integration and High-Performance Computing
Aligning Grains in Time-Series Laboratory Diffraction Contrast Tomography (LabDCT) Data for Machine Learning of Microstructure Evolution
Autonomous approaches for determining structure-processing-property relationships in materials
Categorization of Fracture Surfaces using Deep Learning-enabled 2D Image Analysis
Deep Learning Accelerated Lab-Scale X-Ray Computed Tomography of Low-Melting-Point Solder Alloys Used in Heterogeneously Integrated Semiconductor Packages
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Extraction of Local Scalar 3D Microstructural Properties of SOFC Electrodes from 2D Micrographs using Convolutional Neural Networks.
Feature Extraction from SEM Images of Fatigue Fracture Surfaces
Foundation models for multimodal data mining with applications in materials science.
Hierarchical Bayesian Models for Automating Structural Materials Characterization
Machine Learning Enhanced Data Analytics for Transmission Electron Microscopy
Synthetic 3D Microstructure Generation of Solid Oxide Cell Electrodes using Denoising Diffusion Models

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