Explore ontologies, graph deep learning, and AI in materials science, focusing on advanced manufacturing and synchrotron science. Learn about innovative approaches for multimodal analysis and decision-making.
- Introduction to Ontologies in Materials Science
Definitions and applications of ontologies
Role in data organization and classification
Building and utilizing ontologies in materials science
- Fundamentals of Graph Theory
Concepts of nodes, edges, and graph-based data structures
Key algorithms and their applications in material data
- Graph Deep Learning Techniques
Introduction to graph neural networks (GNN)
Variants of GNNs: GCN, GAT, GraphSAGE
Supervised and unsupervised learning with graphs
- AI Applications in Materials Science
AI-driven discovery of new materials
Multimodal AI approaches for synchrotron-based research
Case studies in advanced manufacturing
- Multimodal Analysis in Materials Science
Integration of multiple data sources (e.g., text, images, spectra)
Techniques for data fusion and representation alignment
AI-based decision-making in complex systems
- Synchrotron Science and AI
Overview of synchrotron facilities and their role in materials analysis
AI methods for synchrotron data interpretation
Enhancing resolution and precision with AI techniques
- Advanced Manufacturing and AI
Smart manufacturing systems and predictive maintenance
Use of AI in process optimization and quality control
Digital twins and simulation-driven design with AI
- Practical Implementation and Tools
Software and tools for ontology development
Graph deep learning frameworks (e.g., PyTorch Geometric, DGL)
Case studies and project work
- Ethical and Societal Implications
Impact of AI and automation on the materials industry
Ethical considerations and sustainability in AI applications
- Future Directions and Innovations
Emerging trends in AI and materials science
Potential research areas and applications
- Final Project and Assessment
Design, development, and presentation of a relevant AI application in materials science
Evaluation based on innovation, technical implementation, and practical impact
