The manufacturing life cycle begins with the discovery of new molecules and materials. This first step is often initiated through computer simulations that explore the space of possible molecules and materials, and identify promising candidates that can later be tested in laboratories. As simulations have grown in scale and complexity, this step has become a critical bottleneck. New data-driven approaches present the opportunity to increase the speed and accuracy of such predictions, with broad potential impact on the US Manufacturing sector. This Harnessing the Data Revolution Institutes for Data-Intensive Research in Science and Engineering (HDR-I-DIRSE) Frameworks award brings together Engineers and Data Scientists to conceptualize a new Engineering Data Science Institute where these tools can be applied for new discovery. The effort will develop new data science approaches to accelerate the engineering life cycle: design, characterization, manufacturing, and operation. This life cycle starts with the discovery of new molecules and materials, followed by advanced characterization with high throughput methods augmented by machine learning. Then, efficient manufacturing and operation of systems that use these materials can be designed and developed. By focusing on this holistic lifecycle, the researchers will build a broadly applicable foundation in Engineering Data Science methods. The new Institute will seek to create an Engineering Data Science environment that supports engineers and scientists (students, postdoctoral researchers, and faculty) through a synergistic set of collaboration and education activities.

This collaborative effort follows three thrusts. The first focuses on the reduction of the experimental design space with data science tools targeting the discovery of new molecules and polymers. The research develops a new, formal framework for pairing accurate predictive simulations with data-driven models to create a scalable and transferable workflow that can be deployed across multiple examples of molecular engineering applications. The second thrust addresses a manifold of cross-cutting needs at the intersection of image data analytics and characterization of materials and systems. It also builds community cyberinfrastructure through open-source software resources with support for execution in public clouds. The final thrust focuses on improving manufacturing, optimization, and control. It further enhances cyberinfrastructure resources through a suite of open-source software solutions to systematically develop digital twin models for complex engineering and manufacturing systems, and apply them for optimization and control. This project is part of the National Science Foundation's Harnessing the Data Revolution (HDR) Big Idea activity and is co-funded by the Office of Advanced Cyberinfrastructure.

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.

National Science Foundation (NSF)
Division of Advanced CyberInfrastructure (ACI)
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Alexis Lewis
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University of Washington
United States
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