Materials data come from diverse methods and communities, each with their own data structures, terminology, and workflows. This makes it difficult to connect and compare data across synthesis, experiment, and computation.

FAIRmat Area D develops common data models and workflow representations that make materials data interoperable, machine-actionable, and AI-ready. By aligning FAIRmat’s data models with community standards, improving data quality, and connecting workflows across domains, Area D provides the common language that enables materials data to be understood and reused across FAIRmat and beyond.

Objectives

• Create consistent data models across synthesis, experiment, and computation.
• Make materials data interoperable with established standards, vocabularies, ontologies, and external data services.
• Enable workflows to be described, exchanged, and reused consistently across scientific domains.
• Improve data quality and semantic richness so that materials data are reliable, machine-actionable, and ready for AI.