Materials Data Sciences: Accelerating Materials Innovation

This course provides a comprehensive introduction to Materials Data Sciences and Informatics, an emerging field that combines materials science with data science and informatics. The curriculum is structured into six modules, covering topics from historical materials development paradigms to advanced data science techniques applied to materials problems. Students will learn about process-structure-property linkages, materials knowledge systems, and how data science can accelerate materials discovery and development. The course explores key concepts such as digital representation of material structures, spatial correlations, principal component analysis, and homogenization techniques. It also covers materials innovation cyberinfrastructure, including databases, e-collaboration platforms, and integrated workflows. Throughout the course, students will engage with real-world case studies and hands-on exercises using tools like PyMKS (Materials Knowledge Systems in Python). This course aims to equip students with the skills needed to apply data science techniques to materials science challenges, preparing them for the future of materials development and innovation.