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Equip the Next-Generation Materials Workforce

Goal 4: Equip the Next-Generation Materials Workforce

  • Pursue New Curriculum Development and Implementation
  • Provide Opportunities for Integrated Research Experiences

For the Nation and materials research community to take full advantage of the MGI framework outlined in previous sections, the next-generation materials workforce must be trained in these new research methods. Students will need access to an education that enables them to work productively in teams whose expertise covers the broad materials spectrum from synthesis and characterization, to theory and modeling, to design and manufacture. In practical terms, students who will go on to become experts in materials synthesis, processing, or manufacture, for example, must have enough training to understand materials modeling and theory, while modelers and theorists must understand the vocabulary and challenges of those who make, characterize, and implement materials solutions. Accomplishing this goal will require continued updatesin the materials science and engineering curricula as well as in departments that contribute to educating the next-generation materials discovery, development, and deployment workforce. Just as many materials science and engineering departments have added computational materials science to their curriculum in recent years, formal instruction on data analytics, uncertainty quantification, and the integration of simulation, experiment, and theory will provide students with the foundation to successfully implement an MGI approach in their academic or industry careers.

The Federal government’s broader activities in science, technology, engineering, and mathematics (STEM) education are driven by the Federal Science, Technology, Engineering, and Mathematics (STEM) Education 5-year Strategic Plan, which identifies five priority areas for STEM education investment. Two of these priority areas, enhancing the STEM experiences of undergraduates and designing graduate education for tomorrow’s STEM workforce, are pivotal for achieving the goals of MGI and the Federal government’s specific activities will be designed to coordinate with the implementation strategies under development in these areas.

Rational Design of Advanced Polymeric Capacitor Films Multidisciplinary University Research Initiative (MURI)

The primary objective of this integrated research program is to design new classes of polymeric materials with high dielectric constant and high breakdown strength, suitable for application in high voltage, high energy density capacitor technologies. We seek to achieve this objective through state-of-the-art "scale-bridging" computations, synthesis, processing, and electrical characterization, and through the creation of a relational database.

PRedictive Integrated Structural Materials Science (PRISMS) Center

At the PRISMS Center integration drives everything we do. Our science is integrated with our computational codes and with the results from our experimentalists who identify new phenomena and fill in missing details. Our Materials Commons repository allows groups to collaborate and share data and provide it to the broader technical community. And our computational software is seamlessly integrating the latest multi-length scale scientific software into open source codes.

Center for Hierarchical Materials Design (CHiMaD)

Center for Hierarchical Materials Design (CHiMaD) is a NIST-sponsored center of excellence for advanced materials research focusing on developing the next generation of computational tools, databases and experimental techniques in order to enable the accelerated design of novel materials and their integration to industry, one of the primary goals of the Obama administration’s Materials Genome Initiative (MGI).

Multidisciplinary University Research Initiative: Managing the Mosaic of Microstructure

The ability to digitally design materials with microstructures optimized to achieve desired properties, is one of the long term goals of the materials field. Simulation-based materials design has the potential to dramatically reduce the need for expensive down-stream characterization and testing. However, this requires reliable algorithms and methodologies that incorporate variability and uncertainty in the design process, and are validated against physics-based models and experiments.