We recently published the Materials Provenance Store (MPS), a database built with the ESAMP SQL schema. In the present work, we ingested MPS into a neo4j database (see Code Availability), in which there is a node for each material "Sample", for each experiment "Process", and for "Sample-Process", which is the application of a Process to a Sample. The experiment provenance for a given sample is encoded through directed edges of type "Next" that connect Sample-Process nodes. Additional nodes for collections of samples, details of each process, data files produced by processes, and analysis results are linked with edges derived from foreign keys in the SQL-based MPS database. We then add additional relationships, such as edges between Element nodes and Sample nodes as well as between pH nodes and electrochemical Process nodes. The encoded knowledge can be further expanded via additional relationships to facilitate data exploration, and relationships can extend to organizational knowledge such as project funding, intended research goal, and relevance to a publication. and 0.1 mA, respectively, each with a duration between 7 and 15 s; and 4) Find all provenances that contain a sequence of 5 electrochemistry experiments in NaOH-based electrolyte wherein the first 4 experiments were each chronoamperometry measurements that produced a measured current above 10 -7 , 10 -8 , 10 -9 , and 10 -10 A, respectively, and the final electrochemistry experiment was a cyclic voltamogram that produced a maximum measured current above 10 -6 A. The query execution times are summarized in Table , demonstrating the excellent performance of the graph-based query across a breadth of query types. For query 1, where the requisite data is indexed in a single SQL table, the SQL-based query is naturally the fastest. For provenance-based queries, the graph-based queries are several times faster than the SQL-based queries. More drastically, the complexity of query 4 revealed a marked difference in query preparation time. While the graph-based query was written in a matter of minutes, initial attempts at writing the SQL query resulted in query timeout after 10 4 s. Multiple days of human effort were required to obtain a query time within a factor of 5 of the graph-based query, which is reflected in the relative complexity of the queries (see Supporting Information). Our conclusion from this exercise is not that graph databases universally outperform the other data management methods with respect to query execution, but rather that the graph-based queries are sufficiently fast for real-time data exploration and can be achieved with intuitive query expressions that avoid complex query engineering.