Simpler Grants.gov #NSF 23-611

NSF Division of Materials Research: Condensed Matter and Materials Theory (CMMT) Funding Opportunity

The U.S. National Science Foundation (NSF) Division of Materials Research is inviting proposals for the Condensed Matter and Materials Theory (CMMT) program. - Government Buyer: - U.S. National Science Foundation (NSF) - Division of Materials Research - Program Focus: - Fundamental theoretical and computational research in materials science - Topical areas include Condensed Matter Physics, Biomaterials, Ceramics, Electronic and Photonic Materials, Metals and Metallic Nanostructures, Polymers, Solid State and Materials Chemistry - Products/Services Requested: - Research services advancing conceptual understanding of hard and soft materials - Development of analytical, computational, and data-centric techniques - Predictive materials-specific theory, simulation, and modeling - Notable Requirements: - Use of advanced methods such as electronic structure calculations, quantum many-body theory, statistical mechanics, Monte Carlo, molecular dynamics, and machine learning - Projects must be led by U.S.-based non-profit, non-academic organizations or institutions of higher education - No specific OEMs or commercial vendors are mentioned, as this is a research grant solicitation - No commercial products or part numbers are requested

Description

The CMMT program supports theoretical and computational materials research in various topical areas including Condensed Matter Physics, Biomaterials, Ceramics, Electronic and Photonic Materials, Metals and Metallic Nanostructures, Polymers, and Solid State and Materials Chemistry. It funds fundamental research to advance understanding of hard and soft materials, develop analytical and computational techniques, and predictive theory and modeling. Research methods include electronic structure, quantum many-body theories, statistical mechanics, Monte Carlo, and molecular dynamics, with emphasis on multi-scale approaches and emerging data-centric methods like machine learning. The program encourages transformative submissions at the frontiers of theoretical, computational, and data-intensive materials research, including new paradigms and machine learning applications.

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