Traditionally, materials design is an iterative process in which the properties of the material are gradually improved by adjusting process by which the material is made. This approach can be accelerated by introducing computational models into the loop and comparing the output with experimental measurements of the structure and properties of the material at each step. The cycle is slowed, however, by the need for humans to be involved at almost every step for data collection, analysis, and reduction. This Designing Materials to Revolutionize and Engineer our Future (DMREF) award supports research to facilitate rapid adoption of advanced materials into engineering applications by leveraging modern data science tools to develop a data framework and infrastructure for seamless integration of experiments and models in complex materials development problems. This framework will be broadly applicable, allowing researchers to adopt it to particular problems and allowing the ideas to be integrated across a wide range of materials and application areas. The concepts and tools developed will be disseminated through freely-available software, open source modules, online tutorials, open access data sets, and training for users to apply the tools in new contexts.

This work seeks to establish a new paradigm for the materials design loop in which the flow of data, rather than individual modeling or experimental tasks, is viewed as central. Modern data science tools will be leveraged to create the semantic framework and physical infrastructure necessary for seamless integration of experiments and models in complex materials development problems. This framework will define and describe classes of material data, and connections among these classes, that are required to create an automated data flow in which each experimental and computational task in the design loop automatically pushes information to, or pulls information from, a data layer common to all tasks. This will accelerate the materials design process by minimizing the required human intervention, while still allowing human input to the loop where essential. A specific instantiation of this approach will be the implementation of a multi-scale modeling framework for the resistance of commercial aluminum alloys to spall failure, with automated connections to advanced microstructural characterization and high-throughput laser shock testing through a centralized data layer. The design loop will be closed by further coupling to models of microstructure development during processing, with machine-learning algorithms trained to optimize the microstructure to resist the nucleation and growth of spall voids.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

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Johns Hopkins University
United States
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