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Funding Opportunity




  Not Verified

Division of Materials Research: Condensed Matter and Materials Theory (DMR:CMMT)

U.S. National Science Foundation (NSF)

Program Title:

Condensed Matter and Materials Theory (CMMT)
 

Synopsis of Program:

CMMT supports theoretical and computational materials research in the topical areas represented in DMR's other Topical Materials Research Programs (these are also variously known as Individual Investigator Award (IIA) Programs, or Core Programs, or Disciplinary Programs), which are: Condensed Matter Physics (CMP), Biomaterials (BMAT), Ceramics (CER), Electronic and Photonic Materials (EPM), Metals and Metallic Nanostructures (MMN), Polymers (POL), and Solid State and Materials Chemistry (SSMC). The CMMT program supports fundamental research that advances conceptual understanding of hard and soft materials, and materials-related phenomena; the development of associated analytical, computational, and data-centric techniques; and predictive materials-specific theory, simulation, and modeling for materials research. First-principles electronic structure, quantum many-body and field theories, statistical mechanics, classical and quantum Monte Carlo, and molecular dynamics, are among the methods used in the broad spectrum of research supported in CMMT. Research may encompass the advance of new paradigms in materials research, including emerging data-centric approaches utilizing data-analytics or machine learning. Computational efforts span from the level of workstations to advanced and high-performance scientific computing. Emphasis is on approaches that begin at the smallest appropriate length scale, such as electronic, atomic, molecular, nano-, micro-, and mesoscale, required to yield fundamental insight into material properties, processes, and behavior, to predict new materials and states of matter, and to reveal new materials phenomena. Approaches that span multiple scales of length and time may be required to advance fundamental understanding of materials properties and phenomena, particularly for polymeric materials and soft matter. Areas of recent interest include, but are not limited to: strongly correlated electron systems; topological phases; low-dimensional materials and systems; quantum and classical nonequilibrium phenomena, the latter including pattern formation, materials growth, microstructure evolution, fracture, and the jamming transition; gels; glasses; disordered materials, hard and soft; defects; high-temperature superconductivity; creation and manipulation of coherent quantum states; nanostructured materials and mesoscale phenomena; sustainable materials; polymeric materials and soft condensed matter; active matter and related collective behavior; biologically inspired materials, and research at the interfaces of materials with biological systems.

CMMT encourages potentially transformative submissions at the frontiers of theoretical, computational, and data-intensive materials research, which includes but is not limited to: i) advancing the understanding of emergent properties and phenomena of materials and condensed matter systems, ii) developing materials-specific prediction and advancing understanding of properties, phenomena, and emergent states of matter associated with either hard or soft materials, iii) developing and exploring new paradigms including computational and data-enabled approaches to advance fundamental understanding of materials and materials related phenomena, iv) fostering research at interfaces among subdisciplines represented in the Division of Materials Research, v) harnessing machine learning or developing explainable machine learning to advance understanding of materials and materials-related phenomena, or vi) developing new theoretical frameworks in areas of materials research, such as active matter, nonequilibrium materials or matter, the synthesis of solid-state materials, or reformulating quantum many-body theory for conceptual insight or greater tractability.

Research involving significant materials research cyberinfrastructure development, for example, software development with an aim to share software with the broader materials community, should be submitted to CMMT through Computational and Data-Enabled Science and Engineering (CDS&E) in accordance with its submission instructions for DMR.

Award Information

Anticipated Type of Award: Standard Grant or Continuing Grant

Estimated Number of Awards: 40

Approximately 40, pending availability of funds.

Anticipated Funding Amount: $15,000,000

$15,000,000, pending availability of funds. Specific information on current awards by the program can be found by using the NSF Award Search engine at https://www.nsf.gov/awardsearch/advancedSearch.jsp to search for awards by Element Code 1765.

Who May Submit Proposals:

Proposals may only be submitted by the following:

  • Institutions of Higher Education (IHEs) - Two- and four-year IHEs (including community colleges) accredited in, and having a campus located in the US, acting on behalf of their faculty members. Special Instructions for International Branch Campuses of US IHEs: If the proposal includes funding to be provided to an international branch campus of a US institution of higher education (including through use of subawards and consultant arrangements), the proposer must explain the benefit(s) to the project of performance at the international branch campus, and justify why the project activities cannot be performed at the US campus.
  • Non-profit, non-academic organizations: Independent museums, observatories, research laboratories, professional societies and similar organizations located in the U.S. that are directly associated with educational or research activities.

 

Who May Serve as PI:

See "Limit on Number of Proposals per PI or Co-PI" below.

Sponsor Institute/Organizations: U.S. National Science Foundation (NSF)

Sponsor Type: Government/Federal

Address: 2415 Eisenhower Ave Alexandria, VA 22314 (703) 292-5111

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Grant

Multiple Deadlines:

Full Proposal Accepted Anytime

Funding Amount:

$15,000,000

40 awards available.

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