Ontologies, Graph Deep Learning, and AI in Materials Science

Explore the integration of ontologies, semantic reasoning, and graph-based deep learning in AI through this Data Science Institute seminar presented by Dr. Pawan Tripathi from Case Western Reserve University. Delve into the paradigm shift in studying high-dimensional multimodal problems within advanced manufacturing, synchrotron science, and photovoltaics. Learn about 'mds-onto', a low-level ontology developed for multiple materials science domains, and discover how foundation models utilizing spatiotemporal graph neural networks enable multimodal analysis. Understand the capabilities of data-driven digital twins (ddDTs) in answering task-specific questions across various manufacturing processes. Gain insights into how incorporating ontologies and knowledge graphs enhances AI intelligence and decision-making, improving process efficiency and product innovation. Explore Dr. Tripathi's expertise in materials data science, interface structural simulations, and developing automated analysis pipelines for large multimodal datasets from diverse experiments.

DSI Seminar | Ontologies, Graph Deep Learning, & AI