propnet: A Knowledge Graph for Materials Science

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. [Display omitted] •The relationships between properties of materials can be represented as a graph•Additional properties can be calculated automatically from an initial dataset•Multiple routes to calculate the same property can be evaluated to assess uncertainty•Available as an open-source Python code, propnet, and interactive website Discovering the ideal material for a new application involves determining its numerous properties, such as electronic, mechanical, or thermodynamic, to match those of its desired application. The rise of high-throughput computation has meant that large databases of material properties are now accessible to scientists. However, these databases contain far more information than might appear at first glance, since many relationships exist in the materials science literature to derive, or at least approximate, additional properties. propnet is a new computational framework designed to help scientists to automatically calculate additional information from their datasets. It does this by constructing a network graph of relationships between different materials properties and traversing this graph. Initially, propnet contains a catalog of over 100 property relationships but the hope is for this to expand significantly in the future, and contributions from the community are welcomed. propnet is a computational framework to explore the network of relationships between fundamental materials properties. There