Opportunity Information: Apply for DE FOA 0002958
The Department of Energy (DOE) Office of Science, through the Advanced Scientific Computing Research (ASCR) program, is soliciting research proposals under the grant opportunity "Scientific Machine Learning for Complex Systems" (Funding Opportunity Number DE-FOA-0002958). The core aim is to fund potentially high-impact research that advances scientific machine learning (SciML) and artificial intelligence (AI) methods specifically for predictive modeling, simulation, and analysis of complex physical systems and processes. The opportunity is framed around the idea that modern science increasingly depends on high-performance computing (HPC), sophisticated numerical models and algorithms, large experimental and observational datasets, and automation to speed up discovery and innovation. In practical terms, DOE is looking for work that helps integrate AI/ML with the kinds of computational workflows used across DOE-relevant science, rather than generic ML that is disconnected from domain physics or simulation realities.
The scientific motivation comes from DOE-wide planning and community reports that identify AI and ML as key tools for science, energy, and national security missions. The announcement explicitly points to application areas spanning materials science, environmental and life sciences, and major physics domains such as high-energy physics, nuclear physics, and plasma physics, along with cross-cutting DOE initiatives like the Energy Earthshots. While the FOA references broad SciML foundations (including domain awareness, interpretability, robustness, and scalability), the primary research emphasis here is narrower: it targets the capability direction focused on Machine Learning-Enhanced Modeling and Simulation (identified as Priority Research Direction #5 in a 2018 DOE Basic Research Needs report) along with uncertainty quantification. That focus signals an interest in methods that do more than analyze data; DOE wants approaches that improve or accelerate simulation, couple ML models with established numerical solvers, and provide credible measures of uncertainty so predictions can be trusted and used in decision-making.
On eligibility, the opportunity is broadly open ("Eligible Applicants: Unrestricted") with one major statutory exception: 501(c)(4) nonprofit organizations that engaged in lobbying activities after December 31, 1995 are not eligible. National laboratories and federally funded research and development centers (FFRDCs) are allowed to participate, either as lead applicants or as team members/subrecipients, but they must follow special DOE administrative pathways. DOE/NNSA national labs, if funded, receive support via the DOE Field-Work Proposal System and conduct the work under their existing DOE contracts, rather than under the standard administrative terms that apply to typical grant recipients. Non-DOE/NNSA FFRDCs are similarly eligible, but awards flow through interagency agreements to the FFRDCs sponsoring federal agencies. By contrast, other federal agencies are explicitly not eligible to apply and cannot be included as subrecipients on another organization’s application.
DOE also highlights a policy priority around broadening participation and investigator diversity. The FOA notes DOE’s continued use of program policy factors in award selection and encourages proposals that are led by, or include partners from, EPSCoR states that are underrepresented in the ASCR portfolio. It also encourages applications led by individuals from groups historically underrepresented in STEM. This is presented as a deliberate effort to diversify both the institutions and people supported by ASCR in this research area.
Key administrative details from the source listing include: the funding instrument is a discretionary grant under CFDA 81.049; the opportunity was created January 24, 2023; and the original closing date was April 19, 2023. The listed award ceiling is $1,200,000. The summary information does not specify the exact number of expected awards in the excerpt provided, but the ceiling indicates DOE anticipates funding projects at a scale consistent with multi-year research efforts or substantial single projects, especially those involving integrated simulation, ML, and uncertainty quantification.
Overall, this FOA is best read as a targeted call for research that pushes the state of the art in SciML methods that work hand-in-hand with computational science: physics- and domain-aware learning, ML that enhances modeling and simulation pipelines, and rigorous uncertainty quantification so AI-enabled predictions for complex systems are robust, scalable on advanced computing platforms, and scientifically defensible across DOE mission-relevant disciplines.Apply for DE FOA 0002958
- The Office of Science in the science and technology and other research and development sector is offering a public funding opportunity titled "Scientific Machine Learning for Complex Systems" 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 2023-01-24.
- Applicants must submit their applications by 2023-04-19. (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 $1,200,000.00 in funding.
- Eligible applicants include: Unrestricted.
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Frequently Asked Questions (FAQs): DOE Office of Science (ASCR) - Scientific Machine Learning for Complex Systems (DE-FOA-0002958)
What is the name of this funding opportunity?
The funding opportunity is titled Scientific Machine Learning for Complex Systems.
Who is offering this grant?
This opportunity is offered by the U.S. Department of Energy (DOE) Office of Science, through the Advanced Scientific Computing Research (ASCR) program.
What is the Funding Opportunity Number (FOA number)?
The Funding Opportunity Number is DE-FOA-0002958.
What is the main purpose of this FOA?
The core aim is to fund potentially high-impact research that advances scientific machine learning (SciML) and artificial intelligence (AI) methods for predictive modeling, simulation, and analysis of complex physical systems and processes, especially in ways that align with DOE-relevant computational workflows.
Is this FOA looking for general machine learning research?
No. The emphasis is on research that integrates AI/ML with scientific and computational realities (for example, simulation and numerical modeling), rather than generic machine learning that is disconnected from domain physics or established computational workflows.
What kinds of approaches does DOE seem to want most?
Based on the description, DOE is especially interested in methods that:
- Enhance or accelerate modeling and simulation (not just data analysis)
- Couple ML models with established numerical solvers and simulation pipelines
- Include rigorous uncertainty quantification so results are credible and usable
- Support trustworthy predictions for complex systems
- Can be robust and scalable on advanced computing platforms (including HPC-oriented workflows)
What is the primary research emphasis called out in the FOA summary?
The FOA is framed around Machine Learning-Enhanced Modeling and Simulation (identified as Priority Research Direction #5 in a 2018 DOE Basic Research Needs report), along with uncertainty quantification.
Why is high-performance computing (HPC) mentioned in the motivation?
The opportunity is motivated by the idea that modern science increasingly depends on high-performance computing, sophisticated numerical models and algorithms, large experimental/observational datasets, and automation to speed discovery. In practical terms, DOE is looking for SciML work that fits into and improves these computational science workflows.
What scientific fields or application areas are referenced?
The announcement points to a broad set of DOE-relevant areas, including:
- Materials science
- Environmental sciences
- Life sciences
- High-energy physics
- Nuclear physics
- Plasma physics
- Cross-cutting DOE initiatives such as the Energy Earthshots
Does the FOA mention foundational SciML qualities like interpretability or robustness?
Yes. The FOA references broad SciML foundations such as domain awareness, interpretability, robustness, and scalability, even though the primary emphasis is more specifically targeted to ML-enhanced modeling/simulation and uncertainty quantification.
Who is eligible to apply?
The listing states Eligible Applicants: Unrestricted, with one major statutory exception described in the summary: certain 501(c)(4) organizations are not eligible (see next question).
Are any nonprofit organizations explicitly ineligible?
Yes. 501(c)(4) nonprofit organizations that have engaged in lobbying activities after December 31, 1995 are not eligible.
Can national laboratories participate?
Yes. National laboratories and federally funded research and development centers (FFRDCs) are allowed to participate as lead applicants or as team members/subrecipients, but they must follow special DOE administrative pathways.
How are DOE/NNSA national laboratories funded if selected?
The summary indicates that DOE/NNSA national labs, if funded, receive support via the DOE Field-Work Proposal System and conduct the work under their existing DOE contracts, rather than under the standard administrative terms that apply to typical grant recipients.
Can non-DOE/NNSA FFRDCs participate, and how would funding work for them?
Yes. Non-DOE/NNSA FFRDCs are eligible, but the summary indicates that awards flow through interagency agreements to the FFRDCs sponsoring federal agencies.
Are other federal agencies allowed to apply?
No. The summary explicitly states that other federal agencies are not eligible to apply.
Can a federal agency be included as a subrecipient on someone else’s application?
No. The summary states that federal agencies cannot be included as subrecipients on another organization’s application.
Does DOE encourage collaboration with underrepresented states or institutions?
Yes. The FOA highlights a policy priority around broadening participation and notes the use of program policy factors in selection. It encourages proposals led by, or including partners from, EPSCoR states that are underrepresented in the ASCR portfolio.
Does DOE address diversity of investigators and research teams?
Yes. The FOA encourages applications led by individuals from groups historically underrepresented in STEM, as part of an effort to diversify both institutions and people supported by ASCR in this research area.
What is the funding instrument for this opportunity?
The funding instrument is a discretionary grant.
What is the CFDA number listed for this opportunity?
The listing identifies the program as CFDA 81.049.
What is the maximum award amount (award ceiling)?
The listed award ceiling is $1,200,000.
Does the summary say how many awards DOE expects to make?
No. The excerpt provided does not specify the number of expected awards.
When was the opportunity created?
The listing states the opportunity was created on January 24, 2023.
What was the original closing date?
The original closing date listed is April 19, 2023.
How should applicants interpret the scope of projects DOE expects to fund?
While the excerpt does not state project duration or award count, the $1,200,000 ceiling suggests DOE anticipates supporting projects at a scale consistent with substantial research efforts, particularly those integrating simulation, ML, and uncertainty quantification for complex systems.
What does "uncertainty quantification" mean in the context of this FOA?
In this FOA context, uncertainty quantification is emphasized as a way to provide credible measures of uncertainty so AI-enabled predictions can be trusted, are scientifically defensible, and can be used in decision-making for complex physical systems and processes.
What is the overall "best read" of the FOA based on the summary?
It is best read as a targeted call for SciML research that works hand-in-hand with computational science: physics- and domain-aware learning, ML that enhances modeling and simulation pipelines, and rigorous uncertainty quantification so predictions for complex systems are robust, scalable, and defensible across DOE mission-relevant disciplines.
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