The MEKG extends the rich use of graph and network models in materials science. Networks have been used to model all known inorganic materials and their interrelationships established with structural and electronic features. Materials knowledge graphs have been established for materials properties and their symbolic or data-driven relationships, for representing interrelationships among various sources of materials data, , for integrating multiple data streams, and for encoding relationships among factual knowledge, analytical models, and domain experts. Knowledge graphs for specific d omains o f materials science have been established for common industrial metals, , nanocomposites, 37 metal organic frameworks, and battery materials. The value proposition for expanding the purview of such knowledge graphs has been made, and the present work builds towards a global materials knowledge graph by establishing best practices for representing experiments and their associated (meta)data in a scalable manner. With the proliferation of graph neural networks, causal modeling, and attention based networks such as transformer models in machine learning writ large, and the expectation that increased deployment for materials dis-