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Meeting 2020 TMS Annual Meeting & Exhibition
Symposium Expanding the Boundaries of Materials Science: Unconventional Collaborations
Presentation Title Innovation in Materials Research Collaborations: DOE Basic Energy Sciences
Author(s) Linda L. Horton
On-Site Speaker (Planned) Linda L. Horton
Abstract Scope Basic Energy Sciences (BES) supports fundamental research to understand, predict, and ultimately control matter and energy at electronic, atomic, and molecular levels to provide foundations for new energy technologies. BES has long-standing strategic planning processes, including BES advisory committee reports and topical in-depth community workshops and reports. Beyond research directions, this planning has defined innovations in how science is funded. Historically, the major funding modality was single investigators at universities and small groups at national laboratories. However, aggressive strategic planning, begun in 2002, identified more complex energy challenges and the need to add a funding modality for large research teams. These recommendations led to Energy Frontier Research Centers, and contributed to Energy Innovation Hubs and Computational Materials Sciences activities. These research teams, often at multiple institutions, can make significant advances by working across disciplines in a focused, synergistic manner. This presentation will overview the success and impact of these modalities.
Proceedings Inclusion? Planned: Supplemental Proceedings volume

OTHER PAPERS PLANNED FOR THIS SYMPOSIUM

Accelerating Materials Design Through Community, Open Data and Collaboration
Additive Manufacturing for Novel Thermal Devices
Convergence: Supporting Multidisciplinary Research at the National Science Foundation
Creating the Next-Generation Materials Genome Initiative Workforce
Innovation in Materials Research Collaborations: DOE Basic Energy Sciences
Integrating Experiment, Data, and Computations to Accelerate the Design of Materials
Machine Learning for Materials Design and Discovery
Mechanical Properties of Molecular Crystals--Connecting with Chemistry
Regularization of Materials Failure Data for Damage Mechanism Categorization by Machine Learning

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