Opportunity Information: Apply for DE FOA 0002875
The Department of Energy (DOE) Office of Science, through its Nuclear Physics (NP) program, issued this discretionary funding opportunity to support research and development projects that apply artificial intelligence and machine learning to the autonomous optimization and control of nuclear physics accelerators and detector systems. The core idea is to move beyond traditional, heavily manual tuning and operations by developing AI/ML-driven approaches that can improve how accelerator facilities and scientific instruments are simulated, controlled, monitored, and analyzed. The opportunity is focused on capabilities that matter for today s NP facilities as well as next generation facilities and instrumentation, with an emphasis on practical improvements that can shorten the cycle from setup and commissioning to reliable operations and ultimately faster scientific discovery.
The solicitation highlights several pain points common to modern accelerator and detector environments. Accelerator complexes and sophisticated detectors generate complex, high dimensional operational data, and they often require frequent tuning, calibration, and adaptation as conditions drift or as experimental configurations change. Likewise, advanced simulations and reconstruction workflows can be computationally expensive and difficult to integrate directly into real time decision making. The DOE is signaling strong interest in R and D that uses AI/ML to tackle these challenges in a way that enables more autonomous or semi autonomous operation, such as smarter feedback and control, faster optimization of machine settings, improved detector performance through automated calibration, and enhanced data acquisition and analysis pipelines that can react intelligently to changing conditions.
Administratively, the program is offered as a DOE grant under Funding Opportunity Number DE FOA 0002875 and falls under the Science and Technology and other Research and Development activity category (CFDA 81.049). Eligibility is listed as unrestricted, meaning a broad range of applicant types may apply if they can meet DOE requirements. The opportunity was created on 2022 11 09 and had an original closing date of 2023 01 11. The stated award ceiling is 2,000,000 dollars per award, indicating DOE anticipated funding projects at a scale suitable for serious algorithm development, integration with facility or instrument controls, and validation using realistic systems or datasets. While the listing notes expected awards without a specific number provided, the ceiling and framing suggest DOE was looking for a select set of strong, facility relevant projects rather than many small exploratory awards.
In short, this FOA is about accelerating progress in nuclear physics research by funding AI/ML methods that can meaningfully improve accelerator and detector performance and reduce the time and effort required to operate, tune, and extract physics results from complex experimental systems. It encourages work that bridges machine learning with real world accelerator and instrumentation constraints, with the end goal of more autonomous, efficient, and robust NP facility operations and faster experimental and computational discovery timelines.Apply for DE FOA 0002875
- The Office of Science in the science and technology and other research and development sector is offering a public funding opportunity titled "Artificial Intelligence and Machine Learning for Autonomous Optimization and Control of Accelerators and Detectors" 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 2022-11-09.
- Applicants must submit their applications by 2023-01-11. (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 $2,000,000.00 in funding.
- Eligible applicants include: Unrestricted.
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Frequently Asked Questions (FAQs)
What is this funding opportunity?
This is a discretionary funding opportunity from the Department of Energy (DOE) Office of Science, Nuclear Physics (NP) program. It supports research and development (R&D) projects that apply artificial intelligence (AI) and machine learning (ML) to the autonomous optimization and control of nuclear physics accelerators and detector systems.
What problem is DOE trying to solve with this FOA?
The FOA targets common operational challenges in modern accelerator facilities and sophisticated detector systems, including:
- High-dimensional, complex operational data that is difficult to use effectively in real time
- Manual, time-intensive tuning, calibration, and operations workflows
- Frequent re-tuning as conditions drift or experimental configurations change
- Computationally expensive simulations and reconstruction pipelines that are hard to integrate into real-time decision-making
The overall aim is to reduce the time and effort required to set up, commission, operate, tune, and extract physics results from complex experimental systems.
What is the main goal of the research being funded?
The main goal is to move beyond traditional, heavily manual tuning and operations by developing AI/ML-driven approaches that improve how accelerator facilities and scientific instruments are simulated, controlled, monitored, and analyzed. The end goal is more autonomous, efficient, and robust operations and faster scientific discovery.
What types of systems are in scope?
The FOA is focused on nuclear physics accelerator facilities and detector systems, including capabilities relevant to today's NP facilities and also next-generation facilities and instrumentation.
What kinds of AI/ML capabilities does DOE emphasize?
Based on the solicitation description, DOE is especially interested in AI/ML R&D that enables more autonomous or semi-autonomous operation, such as:
- Smarter feedback and control approaches
- Faster optimization of machine settings
- Improved detector performance through automated calibration
- Enhanced data acquisition and analysis pipelines that can react intelligently to changing conditions
Does this FOA focus on practical deployment or purely theoretical work?
The emphasis is on practical improvements that can shorten the cycle from setup and commissioning to reliable operations. The solicitation highlights facility- and instrument-relevant work that bridges machine learning with real-world accelerator and instrumentation constraints.
Why is autonomous or semi-autonomous operation a priority?
The FOA notes that accelerator complexes and detectors require frequent tuning and calibration and must adapt as conditions drift or configurations change. AI/ML methods can help systems respond faster and more consistently than manual processes, improving operational efficiency and supporting faster discovery timelines.
What is the Funding Opportunity Number (FOA number)?
The Funding Opportunity Number is DE-FOA-0002875.
Which DOE office and program is sponsoring this opportunity?
This opportunity is issued by the DOE Office of Science through its Nuclear Physics (NP) program.
What is the activity category for this opportunity?
The activity category is Science and Technology and other Research and Development.
What is the CFDA number listed for this opportunity?
The listing identifies CFDA 81.049.
Who is eligible to apply?
Eligibility is listed as unrestricted, meaning a broad range of applicant types may apply as long as they meet DOE requirements.
When was the opportunity created?
The opportunity was created on 2022-11-09.
What was the original application closing date?
The original closing date was 2023-01-11.
What is the maximum award amount (award ceiling)?
The stated award ceiling is $2,000,000 per award.
How large are the projects DOE appears to be targeting?
With an award ceiling of $2,000,000 per award and an R&D scope focused on algorithm development, integration with facility or instrument controls, and validation using realistic systems or datasets, the FOA appears geared toward substantial, facility-relevant projects rather than small exploratory efforts.
How many awards does DOE expect to make?
The listing notes expected awards, but it does not provide a specific number of awards.
Is this funding aimed at accelerators, detectors, or both?
Both. The FOA explicitly supports AI/ML for autonomous optimization and control of nuclear physics accelerators and detector systems, as well as improvements to simulation, monitoring, and analysis workflows connected to those systems.
What kinds of operational data challenges does the FOA mention?
The solicitation points to complex, high-dimensional operational data produced by accelerator complexes and sophisticated detectors, along with the need for frequent tuning and calibration as conditions change.
How does the FOA connect AI/ML to simulation and reconstruction workflows?
The FOA notes that advanced simulations and reconstruction workflows can be computationally expensive and difficult to integrate into real-time decision-making. The opportunity signals interest in AI/ML approaches that can address these challenges in ways that support more responsive operations and analysis.
What is the intended impact on scientific discovery?
The intended impact is faster scientific discovery by reducing the time from setup and commissioning to reliable operations, improving performance and robustness, and enabling more intelligent, adaptive data acquisition and analysis.
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