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.