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.

在线材料数据库的最新发展为数据驱动的材料科学提供了支持。但是，当前的信息学基础设施尚未释放现有数据集中可用的全部知识，也尚未探索不同材料科学领域之间的联系。在这里，我们提供了一个简化的系统，用于在开放源代码Python框架中整理和连接材料属性：propnet。我们演示了此框架增强现有材料属性数据集的能力：通过连续应用物理关系网络来计算相关信息，propnet连接不同的领域知识。除了立即增加可用信息之外，结果还可以检查各组性能之间的相关性，并指导多功能材料的设计。通过强调代码的可扩展性和简便性，我们向材料科学界提供了该软件，以便将其普遍应用于任何实验或计算得出的材料数据库。