Condensed Matter and Materials Theory

The Division of Materials Research: Condensed Matter and Materials Theory (CMMT) program supports theoretical, computational, and data-centric materials research to advance fundamental understanding of hard and soft materials and related phenomena. The program emphasizes predictive modeling, simulation, and analytical techniques to explore material properties across multiple length and time scales. Supported research spans first-principles electronic structure calculations, quantum many-body theories, statistical mechanics, molecular dynamics, and emerging data-driven approaches such as machine learning. Projects are expected to provide insight into material behavior, predict new materials or states of matter, and reveal fundamental mechanisms underlying material properties. The program encourages transformative research that pushes the boundaries of theoretical and computational materials science. Priority areas include emergent properties in condensed matter systems, multi-scale modeling of material phenomena, sustainable materials design, quantum and classical nonequilibrium processes, nanostructured and mesoscale materials, topological phases, strongly correlated electron systems, polymeric and soft condensed matter, and biologically inspired materials. Research addressing collective behavior in active matter, novel frameworks for quantum many-body theory, and innovative data-centric techniques are also of particular interest. Projects developing community-accessible software or materials research cyberinfrastructure should be submitted through the Computational and Data-Enabled Science and Engineering (CDS&E) program. Proposals must demonstrate originality, technical merit, and transformative potential, with a clear articulation of their broader impacts on society and the scientific community. They should address challenges in current theoretical or computational methodologies and propose innovative solutions. Proposals that integrate machine learning or data analytics into material predictions are particularly encouraged. Additionally, the program welcomes multidisciplinary proposals that bridge multiple NSF programs, though co-review arrangements must be specified during submission. Eligibility is limited to accredited U.S. institutions of higher education, nonprofit research institutions, and similar organizations. Investigators are limited to one proposal submission per fiscal year across all Division of Materials Research Topical Materials Research Programs (DMR-TMRP). Proposals for Faculty Early Career Development (CAREER), RAPID, EAGER, and GOALI are exempt from this limit but require prior consultation with program officers. Submissions under the Research in Undergraduate Institutions (RUI/ROA) program follow their respective guidelines but must adhere to CMMT submission constraints. Proposals are accepted at any time, though investigators are advised to avoid submissions between April 15 and June 15 for optimal review timing. Awards are typically in the range of $85,000 to $160,000 per year, for durations of two to four years, contingent upon funding availability. Larger budgets may be considered but must be discussed with the program director in advance. Proposers are required to include a comprehensive Data Management Plan adhering to NSF guidelines, with emphasis on making research data findable, accessible, interoperable, and reusable (FAIR). The evaluation process considers both intellectual merit and broader impacts, including the potential to advance scientific knowledge, societal benefits, and the quality of education and workforce development. Proposals must demonstrate a robust research plan, investigator qualifications, and access to necessary resources. Reviewers will also assess the appropriateness and responsiveness of the Data Management Plan. Investigators are encouraged to consult program officers well in advance to ensure alignment with program priorities and submission guidelines. Non-compliant proposals or those without prior consultation for certain mechanisms will be returned without review.