| Abstract Scope |
One barrier to the adoption of AI for accelerating the design of advanced materials is the lack of robust mechanisms to manage the required experimental and simulation data and expert knowledge. Materials data management is particularly challenging due to the multimodal nature of the data, which can include numeric data, images, notes, and more. Further compounding the challenge is the different scales of data, from small (e.g., KB to MB) to big (e.g., TB or more). To address these challenges, GE Research has developed a knowledge-driven materials informatics platform that enables non-computer scientists to explore a knowledge graph model of the domain, to query and analyze data captured in different repositories. This talk will provide an overview of the platform, its strengths and limitations, and discuss its application to two use cases: (i) additive manufacturing process parameter optimization for a nickel-base superalloy, and (ii) the development of high entropy alloys. |