|Title||propnet: A Knowledge Graph for Materials Science|
|Publication Type||Journal Article|
|Year of Publication||2020|
|Authors||David Mrdjenovich, Matthew K Horton, Joseph H Montoya, Christian M Legaspi, Shyam Dwaraknath, Vahe Tshitoyan, Anubhav Jain, Kristin A Persson|
|Pagination||464 - 480|
Data-driven materials science is bolstered by the recent growth of online materials databases. However, the current informatics infrastructure has yet to unlock the full knowledge available within existing datasets or to explore connections between different materials science domains. Here, we present a streamlined system for codifying and connecting materials properties in an open-source Python framework: propnet. We demonstrate the capability of this framework to augment existing datasets of materials properties: by consecutively applying a network of physical relationships to calculate related information, propnet connects disparate domain knowledge. Beyond an immediate increase in available information, the results allow for the examination of correlations between sets of properties and guide the design of multifunctional materials. By emphasizing code extensibility and simplicity, we offer this software to the materials science community for general application to any experimental or computationally derived materials database.