The discovery and development of new materials with unique properties and functionalities has revolutionized entire industries, including aviation, space, communication, biomedical, and automotive. Materials design has been traditionally experimentally and computationally intensive. However, advances in data-driven approaches, computational power, and experimental capabilities have created a tipping point for targeted and efficient materials design. This Harnessing the Data Revolution Institutes for Data-Intensive Research in Science and Engineering (HDR-I-DIRSE) Frameworks award supports conceptualization of an Institute to advance data-intensive research in Materials Science and Engineering. The IDEAS^2 (Integrated Data Environment for Accelerated Stochastic Science) Institute for Materials Discovery will provide a platform for the development of experimental and computational frameworks for materials advancement, that encourages collaboration and the sharing of data-driven approaches among research communities. The Data Science methods are intrinsically interoperable, and this program will engage diverse research communities in the collaborative development of large data frameworks that are applicable across a wide range of disciplines. The IDEAS^2 Institute will be structured to lower the barrier for domain scientists to work with data scientists through a variety of mechanisms including biannual "Teach the Teacher" workshops, an annual IDEAS^2 Symposium, visiting faculty positions at UCSB, and a range of other community engagement activities. Students working on this program will gain valuable multidisciplinary research and educational opportunities.
First-principle calculations of thermodynamic and kinetic properties and information from microstructurally-based, high throughput models will be integrated into the design of data structures and the analyses of the developed techniques. The developed frameworks will be grounded in machine learning approaches that are fundamentally-based, computationally and statistically tractable, and incorporate domain knowledge and simulation results. The frameworks and data developed in the Institute - such as those to predict processing advancements from first principles, model these advancements in a high-throughput fashion, enable high-throughput experimentation, align the resulting experimental data (chemical, microstructure, deformation, etc.), and efficiently mine the resultant high-dimensional datasets - will be integrated with an open-source platform (BisQue) to facilitate both internal and external collaboration on their development for a broad range of materials applications. The computational infrastructure and parallelization of calculations through the BisQue platform enables the screening of very large datasets, with a hierarchical workflow requiring minimal software requirements (only a web browser is needed) and minimal domain knowledge of the user in modeling of materials. The focus of this program is on a research area with major and broad implications on numerous scientific and technological fields, and it also represents a unique training opportunity with acquired skills that will propel its graduates to the forefront of the emerging, critical field of data-driven science, as well as its many application areas within various scientific disciplines and high-tech industry sectors. This project is part of the National Science Foundation's Harnessing the Data Revolution (HDR) Big Idea activity and is co-funded by the Division of Civil, Mechanical and Manufacturing Innovation.
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.
