This program has been archived.
Division of Materials Research
Computational and Data driven Materials Research (CDMR)
|Diana Farkasemail@example.com||(703) 292-2335|
|Program Director: Dr. Diana Farkas|
Important Information for Proposers
A revised version of the NSF Proposal & Award Policies & Procedures Guide (PAPPG) (NSF 22-1), is effective for proposals submitted, or due, on or after October 4, 2021. Please be advised that, depending on the specified due date, the guidelines contained in NSF 22-1 may apply to proposals submitted in response to this funding opportunity.
The Division of Materials Research recognizes the scientific opportunities enabled by advances in theory and computation and the application of data-enabled and data-centric approaches in conjunction with experimental activities to advance materials research. This program supports materials research driven by computation, data, or theory. Areas of interest include but are not limited to new materials design and preparation, structure development, evolution and control, nanoscale materials, multi-scale properties and optimization in all the topical, disciplinary, and interdisciplinary areas represented in DMR programs. Research and education activities supported in this program are distinct from those supported in other programs in DMR by approach: successful projects may be more simulations and less algorithm development and theory than in CMMT and may also incorporate experiments and/or a heavy emphasis on data mining. Supported projects will advance fundamental understanding of materials or materials-related phenomena through transformative research in which a computational, data-centric, or theoretical activity drives a well-integrated experimental activity or vice versa. Successful projects may involve the creation of software or databases validated by associated synthesis or experiments to address imperfect or incomplete theoretical understanding or computational intractability to forge innovative methods to advance the frontiers of materials research. Projects that explore the combination of dedicated computation or data-centric activities with innovative instrumentation leading to transformative tools to advance materials research will also be considered. Of particular interest are projects that create new paradigms for materials research through the innovative use of digital data in ways that complement or dramatically enhance traditional computational, experimental, and theoretical methods to discover new materials or new materials-related phenomena, and advance the fundamental understanding of materials more generally. This program will also support efforts to develop materials research knowledge portals that integrate experimental data with data of simulation or theoretical origin with the aim to organize, enhance, and make broadly accessible the digital products of materials research.