Opportunity Information: Apply for DE FOA 0001528
Apply for DE FOA 0001528
- The PAMS-SC in the science and technology and other research and development sector is offering a public funding opportunity titled "Computational Materials Sciences" and is now available to receive applicants.
- Interested and eligible applicants and submit their applications by referencing the CFDA number(s): 81.049.
- This funding opportunity was created on Feb 05, 2016 and posted on Feb 05, 2016.
- Applicants must submit their applications by Apr 25, 2016. (Agency may still review applications by suitable applicants for the remaining/unused allocated funding in 2026.)
- Each selected applicant is eligible to receive up to $2,500,000.00 in funding.
- The number of recipients for this funding is limited to 2 candidate(s).
- Eligible applicants include: Unrestricted (i.e., open to any type of entity above), subject to any clarification in text field entitled Additional Information on Eligibility.
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Opportunity Summary:
The U.S. Department of Energy's Office of Basic Energy Sciences (BES) is seeking grant applications for its Computational Materials Sciences program, focused on building large, integrated research teams that combine expertise across disciplines. The core purpose of this opportunity is to accelerate the predictive design of functional materials by developing validated, community-facing software codes and shared databases. A key point is that the scope is aimed at functional materials and explicitly excludes structural materials, signaling that the program is prioritizing materials where performance is tied to electronic, magnetic, optical, catalytic, ionic, or similar functional behavior rather than load-bearing mechanical applications.
The program is centered on producing open, broadly usable research infrastructure rather than one-off, single-lab tools. BES is looking for teams that can create "community codes" meaning software intended to be adopted, extended, and relied on by a wide range of researchers, supported by rigorous validation and accompanied by data resources that enable predictive modeling. In addition to traditional computational materials modeling, the opportunity highlights the growing importance of handling and learning from large datasets. That includes experimental data streams from advanced materials characterization methods, as well as data produced during synthesis, processing, and property measurements. It also includes the massive volumes of simulation data generated by large-scale computational campaigns that model materials phenomena across length and time scales. In practice, this points toward projects that connect experiments, computation, and data science so that materials predictions are not only theoretically sophisticated, but also anchored to measurable reality and usable for design decisions.
This funding opportunity is explicitly positioned as part of DOE's ongoing support for the Materials Genome Initiative (MGI) for Global Competitiveness, launched in 2011. The MGI's headline goal is to cut the time from discovery to deployment of new materials by half, with a strong link to strengthening U.S. manufacturing competitiveness. In that context, the solicitation emphasizes research approaches that make materials development faster and more reliable, particularly by improving predictive capability, standardizing and sharing reusable computational tools, and ensuring that codes can scale effectively on modern high-performance computing systems. The announcement specifically references leveraging today's petascale resources and preparing for future exascale leadership computing facilities, implying an expectation that proposed software and workflows will be designed to run efficiently at very large scale.
From an administrative standpoint, this is a discretionary DOE funding opportunity using the grant instrument under the Science and Technology and other Research and Development activity category, with CFDA number 81.049. Eligibility is listed as unrestricted, meaning it is broadly open to different types of applicant organizations, subject to any additional eligibility clarifications in the full announcement. The funding opportunity number is DE-FOA-0001528. The closing date for applications was April 25, 2016 (original and current closing date are the same), and the agency name is listed as PAMS-SC. BES anticipated making about two awards, with an award ceiling of $2,500,000, which signals a relatively small number of larger, team-oriented projects rather than many smaller individual investigator awards.
Overall, the opportunity is best understood as a push to build durable, openly available computational and data foundations for functional materials innovation: multi-institution or multi-discipline teams; validated and shared code and data products; tight coupling of modeling with experimental and processing datasets; and strong alignment with large-scale computing so the resulting tools can power faster, more predictive materials discovery and development.
Frequently Asked Questions (FAQs)
What is this funding opportunity?
This is a U.S. Department of Energy (DOE) Office of Science opportunity from the Office of Basic Energy Sciences (BES) under the Computational Materials Sciences program. It seeks grant applications to support large, integrated research teams focused on accelerating predictive design of functional materials through shared, validated software and data resources.
Which DOE office and program are sponsoring this?
The sponsor is DOE's Office of Basic Energy Sciences (BES), and the specific program referenced is Computational Materials Sciences.
What is the main purpose of the Computational Materials Sciences program in this solicitation?
The purpose is to accelerate predictive design of functional materials by developing validated, community-facing software codes ("community codes") and shared databases that can be broadly used by the research community.
What kinds of research outputs is BES looking for?
The solicitation emphasizes durable, open research infrastructure rather than one-off tools. Key outputs include validated software codes intended for broad adoption and extension by the community, plus shared databases that support predictive modeling and design decisions.
What does "community code" mean in this context?
"Community code" refers to software that is intended to be adopted, relied on, extended, and maintained for use by a wide range of researchers, rather than being built for a single lab or one specific project. The description also stresses rigorous validation and accompanying data resources.
Is the scope focused on functional materials or structural materials?
The scope is aimed at functional materials and explicitly excludes structural materials. In other words, it prioritizes materials where performance is tied to electronic, magnetic, optical, catalytic, ionic, or similar functional behavior, rather than load-bearing mechanical applications.
What kinds of functional behavior are emphasized?
The opportunity description points to materials with electronic, magnetic, optical, catalytic, ionic, or similar functional properties as central to the program's focus.
Does the opportunity emphasize individual investigator projects or larger teams?
It emphasizes building large, integrated research teams that combine expertise across disciplines, signaling a team-oriented approach rather than single-investigator efforts.
Are multi-disciplinary or cross-domain approaches expected?
Yes. The description highlights integrating expertise across disciplines and connecting experiments, computation, and data science so that predictions are grounded in measurable reality and usable for design.
How important are data resources in the proposed work?
They are central. The solicitation calls for shared databases and highlights handling and learning from large datasets, including experimental data streams, synthesis/processing/property measurement data, and large-scale simulation datasets.
What kinds of datasets are specifically mentioned as relevant?
The opportunity references large datasets from advanced materials characterization methods, data produced during synthesis, processing, and property measurements, and massive volumes of simulation data from large-scale computational campaigns spanning length and time scales.
Is there an expectation to connect computation with experimental data?
Yes. The description points toward projects that connect experiments, computation, and data science so that computational predictions are anchored to measurable reality and can support design decisions.
How does this opportunity relate to the Materials Genome Initiative (MGI)?
It is explicitly positioned as part of DOE's ongoing support for the Materials Genome Initiative (MGI) for Global Competitiveness (launched in 2011). The MGI goal highlighted is cutting the time from discovery to deployment of new materials by half and strengthening U.S. manufacturing competitiveness.
What does the solicitation emphasize in terms of speeding materials development?
It emphasizes improving predictive capability, standardizing and sharing reusable computational tools, and ensuring that codes can scale effectively on modern high-performance computing systems to make materials development faster and more reliable.
Are high-performance computing (HPC) considerations part of the requirements?
Yes. The announcement references leveraging today's petascale resources and preparing for future exascale leadership computing facilities, implying software and workflows should be designed to run efficiently at very large scale.
What is the funding opportunity number?
The funding opportunity number is DE-FOA-0001528.
What is the CFDA number for this opportunity?
The CFDA number is 81.049.
What type of funding instrument is used?
This is a discretionary DOE funding opportunity using the grant instrument under the Science and Technology and other Research and Development activity category.
Who is the agency listed for this opportunity?
The agency name is listed as PAMS-SC.
Who is eligible to apply?
Eligibility is listed as unrestricted, meaning it is broadly open to different types of applicant organizations, subject to any additional eligibility clarifications in the full announcement.
How many awards were anticipated?
BES anticipated making about two awards.
What is the maximum award size (award ceiling)?
The award ceiling is $2,500,000.
What does the anticipated number of awards and ceiling suggest about project size?
With about two anticipated awards and a $2,500,000 ceiling, the solicitation signals a small number of larger, team-oriented projects rather than many smaller awards.
When was the application closing date?
The closing date for applications was April 25, 2016, and the original and current closing date are the same.
Is this opportunity primarily about creating new materials, or creating tools and infrastructure?
Based on the description, it is primarily a push to build durable, openly available computational and data foundations (validated codes and shared databases) that enable faster, more predictive materials discovery and development.
What overall project characteristics does BES appear to be prioritizing?
The description emphasizes: multi-institution or multi-discipline teams; validated and shared code and data products; tight coupling of modeling with experimental and processing datasets; and strong alignment with large-scale computing so tools can be broadly used for predictive materials design.
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