The National Science Foundation (NSF) is offering a federal grant opportunity titled "Condensed Matter and Materials Theory" aimed at supporting theoretical and computational materials research across various topical areas. This program encourages proposals that advance the understanding of materials properties and phenomena, utilizing innovative computational and data-centric techniques, including machine learning and data analytics. The total estimated funding for this program is $15 million, with proposals accepted on a rolling basis. Interested applicants can find more information and submit inquiries via the NSF grants.gov support email at grantsgovsupport@nsf.gov.

CMMT supports theoretical and computational materials research in the topical areas represented in DMR&apos;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&amp;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.

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NSF grants.gov support&lt;br/&gt;grantsgovsupport@nsf.gov

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