EAGER: A DATA-INTENSIVE INSTRUMENT FOR STRONGLY CORRELATED SYSTEM MATERIAL DESIGN

Project Details

Description

Technical Description:This award from the National Science Foundation to Rutgers University in New Brunswick is for the development of a new computer architecture with multiple levels of interconnected memory, optimized for the simulation of strongly correlated materials from first principles calculations. Strongly correlated materials have the potential to be transformative, two examples: thermoelectric materials, which can generate electricity from waste heat with high efficiency; and, superconductors with potential for higher critical temperatures, fields, and currents, which can revolutionize the electric grid by reducing transmission losses. The use of computational methods to accelerate the pace of discovery of materials with desirable properties is one of the greatest challenges in condensed matter science. Materials with strongly correlated electron systems are particularly difficult to simulat because their physical properties cannot be accurately represented in terms of a system of independent particles moving in an average potential, thus requiring new methodologies and powerful supercomputers for their theoretical description. Non-Technical Description:This award from the National Science Foundation to Rutgers University in New Brunswick is for the development of a new computer architecture optimized for the dsicovery of new materials from first principles calculations. The new computer will enable collaborations between material synthesis groups and computational physicists. The instrument will drive computer science research, will be used as a resource for teaching computational science and engineering at Rutgers, and will serve as a prototype for a future supercomputer in the national cyber-infrastructure.
StatusFinished
Effective start/end date9/1/138/31/14

Funding

  • National Science Foundation (National Science Foundation (NSF))

Fingerprint

Explore the research topics touched on by this project. These labels are generated based on the underlying awards/grants. Together they form a unique fingerprint.