The development and discovery of new materials can be significantly enhanced through the adoption of FAIR (Findable, Accessible, Interoperable, and Reusable) data principles and the establishment of a robust data infrastructure in support of materials informatics. A FAIR data infrastructure and associated best practices empower materials scientists to access and make the most of a wealth of information on materials properties, structures, and behaviors, allowing them to collaborate effectively, and enable data-driven approaches to material discovery. To make data findable, accessible, interoperable, and reusable to materials scientists, we developed and are in the process of expanding a materials data infrastructure to capture, store, and link data to enable a variety of analytics and visualizations. Our infrastructure follows three key architectural design philosophies: (i) capture data across a federated storage layer to minimize the storage footprint and maximize the query performance for each data type, (ii) use a knowledge graph-based data fusion layer to provide a single logical interface above the federated data repositories, and (iii) provide an ensemble of FAIR data access and reuse services atop the knowledge graph to make it easy for materials scientists and other domain experts to explore, use, and derive value from the data. This paper details our architectural approach, open-source technologies used to build the capabilities and services, and describes two applications through which we have successfully demonstrated its use. In the first use case, we created a system to enable additive manufacturing data storage and process parameter optimization with a range of user-friendly visualizations. In the second use case, we created a system for exploring data from cathodic arc deposition experiments to develop a new steam turbine coating material, fusing a combination of materials data with physics-based equations to enable advanced reasoning over the combined knowledge using a natural language chatbot-like user interface.

通过采用公平（可查找、可访问、可互操作和可重用）数据原则并建立强大的数据基础设施来支持材料信息学，可以显着增强新材料的开发和发现。 FAIR 数据基础设施和相关最佳实践使材料科学家能够访问并充分利用有关材料属性、结构和行为的大量信息，使他们能够有效协作，并实现数据驱动的材料发现方法。为了使材料科学家能够查找、访问、互操作和重用数据，我们开发并正在扩展材料数据基础设施，以捕获、存储和链接数据，从而实现各种分析和可视化。我们的基础设施遵循三个关键的架构设计理念：（i）跨联合存储层捕获数据，以最小化存储占用空间并最大化每种数据类型的查询性能，（ii）使用基于知识图的数据融合层提供单一(iii) 在知识图谱之上提供公平数据访问和重用服务的集合，使材料科学家和其他领域专家能够轻松探索、使用数据并从数据中获取价值。本文详细介绍了我们的架构方法、用于构建功能和服务的开源技术，并描述了我们成功演示其用途的两个应用程序。在第一个用例中，我们创建了一个系统，通过一系列用户友好的可视化功能来实现增材制造数据存储和工艺参数优化。在第二个用例中，我们创建了一个系统，用于探索阴极电弧沉积实验的数据，以开发新型蒸汽涡轮机涂层材料，将材料数据与基于物理的方程相结合，从而使用自然语言对组合知识进行高级推理类似聊天机器人的用户界面。