Condensed Matter and Materials Theory
🏛 U.S. National Science Foundation (NSF)
✓ Free, no account · Source: Grants.gov · Last verified Jul 17, 2026
Can you apply?
This grant is for theoretical and computational materials research conducted by faculty at U.S. universities and non-profit research institutions. Eligible applicants include accredited two- and four-year institutions of higher education (including community colleges) and non-profit, non-academic organizations like independent research laboratories and museums located in the U.S.
Research must advance fundamental understanding of hard and soft materials through theory, computation, simulation, or modeling. Supported methods include first-principles electronic structure, quantum many-body theories, Monte Carlo simulations, molecular dynamics, and machine learning approaches.
The program welcomes research across condensed matter physics, biomaterials, ceramics, electronic materials, metals, polymers, and materials chemistry. Projects at subdisciplinary interfaces and potentially transformative work at research frontiers are encouraged.
International branch campuses of U.S. institutions may participate, but applicants must justify why work cannot be performed at the U.S. campus.
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Program description
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.
Additional Information
Eligibility rules apply for submissions; please see Section II. Program Description, Section IV. Eligibility Information, and Section V.A Proposal Preparation Instructions.
Who can apply
Eligible applicants
How to apply
Application links
Required documents
- Project Narrative (research plan)
- Budget and Budget Justification
- Biographical Sketches (PI and Senior Personnel)
- Facilities and Resources
- References Cited
Program contact
- 👤 National Science Foundation
- 📧 grantsgovsupport@nsf.gov
- 📞 703-292-4261
Funding track record
Recent awards under CFDA 47.049 from the last 3 years — real organizations that won funding through this same program.
Top 10 Largest Recent Awards
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$570,618,065
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$480,514,346
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$411,651,013
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$277,033,094
-
$236,459,999
-
$190,969,692
-
$159,846,534
-
$144,261,921
-
$140,880,752
-
$124,000,000
Top States by Funding
- VA 5 awards $960.5M
- DC 4 awards $907.5M
- CA 12 awards $589.6M
- AZ 8 awards $566.5M
- NY 7 awards $319.9M
Source: USAspending.gov — federal spending transparency. Data covers last 3 years.
Funding history
Annual funding for this program — Federal obligations (CFDA 47.049). How funding has trended year over year.
| 2024 | $1,539,910,000 | |
| 2025 | $1,537,650,000 | |
| 2026 est. | $512,280,000 |
FAQ
Who can apply for CMMT funding?
Faculty at accredited U.S. colleges and universities can apply through their institution. Non-profit research organizations like independent labs and museums also qualify if located in the U.S.
What types of research does CMMT support?
The program funds theoretical, computational, and data-intensive materials research. This includes electronic structure calculations, simulations, machine learning for materials discovery, and multiscale modeling of materials properties.
Does this grant require cost-sharing or institutional match?
No cost-sharing is required. You can request full funding for eligible project costs.
What makes an application competitive?
Strong applications demonstrate novel theoretical insights, rigorous computational methods, potential for transformative discovery, and clear connections to experimental materials research. Work at interdisciplinary interfaces is encouraged.
Are there page limits or specific document requirements?
NSF typically requires a project narrative, budget, and supplementary documents like CV summaries. Check the most recent solicitation for exact page limits and formatting requirements.
💡 Tips for applicants
- Align your proposal with one of the topical areas: condensed matter physics, biomaterials, ceramics, electronic materials, metals, polymers, or materials chemistry.
- Emphasize the fundamental scientific advance and why your theoretical or computational approach is necessary to achieve it.
- If using machine learning, explain how it advances materials understanding, not just prediction accuracy.
- Connect your work to experimental materials research or explain how theory predicts new materials or phenomena.
- Clearly describe computational methods and scales of investigation (electronic, atomic, nano-, micro-, mesoscale) relevant to your science.
⚠️ Common mistakes
Proposals that focus on applied development or engineering optimization rather than fundamental materials understanding tend to be rejected. Submissions lacking rigorous theoretical or computational justification, or those proposing machine learning without clear connection to materials insights, are less competitive. Vague descriptions of computational methods or failure to justify why the chosen scale of modeling is necessary weaken applications.
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