Simpler Grants.gov #23-611

NSF Solicitation for Theoretical and Computational Materials Research Grants

The U.S. National Science Foundation (NSF) is inviting proposals for its Condensed Matter and Materials Theory (CMMT) program, supporting advanced theoretical and computational research in materials science. - Government Buyer: - U.S. National Science Foundation (NSF), Division of Materials Research - Products/Services Requested: - Fundamental research grants in areas such as condensed matter physics, biomaterials, ceramics, electronic and photonic materials, metals, polymers, and materials chemistry - Research methods may include: - Electronic structure calculations - Quantum many-body theory - Statistical mechanics - Monte Carlo simulations - Molecular dynamics - Data-centric approaches (e.g., machine learning) - Software development for materials research cyberinfrastructure is encouraged - OEMs and Vendors: - No specific OEMs or vendors are named; this is a grant opportunity for research projects - Unique or Notable Requirements: - Proposals must justify any work performed at international branch campuses of U.S. institutions - Eligible applicants include U.S.-based non-profits, research labs, professional societies, and accredited higher education institutions - Interdisciplinary and transformative research is encouraged

Description

The Condensed Matter and Materials Theory (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. The program 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 theory, statistical mechanics, Monte Carlo, and molecular dynamics, with emphasis on multi-scale approaches and emerging data-centric methods such as machine learning. The program encourages transformative submissions at the frontiers of theoretical, computational, and data-intensive materials research, including new paradigms and interdisciplinary work.

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