The Materials Experiment Knowledge Graph

Materials knowledge is inherently hierarchical. High-level data descriptors can be provided by the chemical elements, crystal structure motifs, and types of materials properties, although a given piece of data must ultimately be considered in the context of its acquisition. Detailed descriptors of a piece of experimental data include not only the metadata for the experiment that generated it, but also the prior history of synthesis and metrology experiments. Graph databases offer an opportunity to represent such hierarchical relationships among data, organizing semantic relationships into a knowledge graph. Initial reports of knowledge graphs in materials science highlight the breadth of approaches for their development. Herein, we establish a knowledge graph of materials experiments whose construction encodes the complete provenance of each material sample and its associated experimental data and metadata. Additional relationships among materials and experiments further encode knowledge and facilitate data exploration. The Materials Experiment Knowledge Graph is sufficiently large and complex to demonstrate a path toward a global materials knowledge graph. We characterize the scalability of this approach, especially with respect to executing queries, illustrating the value that modern graph databases can provide to the enterprise of data-driven materials science.