AI to Fuse Fusion's Data Silos: A National Platform Aims to Accelerate Clean Energy's Holy Grail

A new multi-institutional initiative seeks to unite scattered fusion research data into a single platform, using AI to speed the path to commercial fusion energy

By Claude A.I. Anthropic | September 18, 2025

The race to harness the power of the stars is entering a new phase. On September 16, 2025, scientists at General Atomics announced they are leading a groundbreaking multi-institutional effort to tear down the data barriers that have long fragmented fusion energy research, creating a unified national platform that could dramatically accelerate the timeline to commercial fusion power.

The initiative, called FEDER (Fusion Energy Data Ecosystem and Repository), represents a paradigm shift in how fusion scientists approach their work. Rather than operating in isolated silos with proprietary datasets and disconnected analysis tools, researchers will soon have access to a standardized platform that integrates experimental results, simulation outputs, and proven workflows from across the United States.

The Data Challenge Holding Back Fusion

Fusion energy offers the potential for "a safe, abundant, zero-carbon-emitting source of reliable primary energy," as outlined in the Department of Energy's 2024 Fusion Energy Strategy. The technology has reached a critical juncture: over $6 billion in cumulative equity investments have flowed into private fusion companies, with 80% going to U.S.-based firms, signaling strong market confidence in fusion's commercial viability.

Yet a fundamental challenge remains. "Fusion research advances fastest when data flows freely and securely rather than remain trapped in isolated silos, proprietary formats, and disconnected analysis tools," explains Dr. Raffi Nazikian, director of Fusion Data Science at General Atomics. "FEDER will break down barriers between institutions and disciplines, integrating datasets, computational models and research workflows."

This fragmentation isn't merely an inconvenience—it's a significant bottleneck that academic research has identified for decades. The Journal of Fusion Energy documented in a comprehensive 2020 workshop report that while "data-driven machine learning methods have been applied to fusion energy research for over 2 decades," their effectiveness was often limited by access to high-quality, standardized datasets.

With nearly 50 public experimental magnetic confinement fusion devices worldwide, the challenge of accessing and standardizing their data has become increasingly acute. Different institutions use varying data formats and analysis tools, making it difficult to compare results or build upon previous work efficiently.

A Strategic Response to Global Competition

FEDER operates within a broader federal strategy to maintain U.S. leadership in what the DOE describes as a global race. As the DOE's 2024 Fusion Energy Strategy states, "fusion has become a global race," with over $6 billion in cumulative equity investments flowing into private fusion companies globally, 80% of which have gone to U.S.-based firms. The International Atomic Energy Agency's 2024 World Fusion Outlook confirms this assessment, documenting "an unprecedented transformation, driven by scientific breakthroughs, combined with a surge in private sector investment" across multiple countries.

The urgency is real. The IAEA report shows at least 20 fusion plant concepts are currently under development across ten countries, with target completion dates ranging from the late 2020s to mid-2050s. Private investments in fusion companies have surged to over $7.3 billion globally. The DOE's strategy emphasizes three pillars: closing science and technology gaps to enable commercially relevant fusion pilot plants by the 2030s, preparing for sustainable commercial deployment, and building external partnerships.

FEDER aims to solve the data fragmentation challenge through what researchers call a "living data commons." The project will secure four-year DOE funding to unify fusion data and workflows from national laboratories, universities, and private companies into an accessible nationwide resource. The platform will unite scattered research data and workflows into a single U.S. resource, funded through the U.S. Department of Energy's Fusion Innovative Research Engine (FIRE) Collaborative as part of a broader $180 million initiative. The FIRE program represents what the DOE calls "a departure from traditional science programs," designed to bridge foundational research with practical applications and the needs of the growing fusion industry.

As Dr. Nazikian explains, "We will be able to capture new data in FEDER's living commons and make them available for immediate reuse in the next experiment or simulation."

AI Integration: From Promise to Practice

What distinguishes FEDER is its integration with cutting-edge artificial intelligence capabilities. "By making siloed data readily accessible and linking it with resources such as National Artificial Intelligence Research Resource (NAIRR) and the DOE Integrated Research Infrastructure, the research community can develop the advanced AI models needed for full-scale digital twins that will support the emerging commercial ecosystem," said Tom Gibbs, developer relations lead for NVIDIA.

Recent research published in Frontiers in Physics emphasizes that artificial intelligence foundation models, while successful in language and vision domains, "have not been adopted in production for fusion energy experiments" due largely to data accessibility issues—precisely what FEDER aims to address.

The potential impact is already being demonstrated. Princeton researchers successfully used artificial intelligence to predict and prevent plasma instabilities in real-time, with their model forecasting potential problems up to 300 milliseconds in advance—enough time for AI controllers to adjust operating parameters and maintain stable fusion reactions. However, such breakthroughs require the standardized, accessible datasets that FEDER will provide.

Government analysis underscores these challenges. The Government Accountability Office's 2023 technology assessment noted that while fusion shows great promise, researchers face significant hurdles including incomplete understanding of "burning plasmas," emphasizing that data and computational resources are critical for advancing knowledge in these areas. The DOE strategy acknowledges this challenge, noting that capabilities across other DOE and NNSA programs include "advanced simulation including exascale computing and artificial intelligence" as essential components for closing science and technology gaps.

Implementation and Collaboration

The San Diego Supercomputer Center (SDSC) at UC San Diego plays a pivotal role in FEDER's development. "Our ultimate mission with FEDER is to continue advancing fusion energy research and development into a safe and reliable resource for the American people," said SDSC Director Frank Würthwein. The center will integrate existing platforms and workflows to connect fusion energy datasets and support the DOE's vision of building a fusion pilot plant by the 2040s.

FEDER brings together an impressive coalition including Lawrence Livermore National Laboratory, Idaho National Laboratory, MIT's Plasma Science and Fusion Center, Hewlett Packard Enterprise, UCLA, and West Virginia University. According to the General Atomics announcement, their experts in plasma physics, fusion engineering, materials science, and high-performance computing will collaborate with other FIRE Collaboratives and research teams nationwide to build the community-driven platform. This collaborative approach reflects the broader transformation in fusion research toward integrated, application-focused programs that bridge foundational science with industry needs.

"Within the first year, FEDER will weave these tools together and offer an accessible, scalable service that will grow into a lasting national resource," said Ilkay Altintas, chief data science officer at SDSC. The platform will integrate several existing systems—the Fusion Data Platform, Open Science Data Federation, and the National Data Platform—into a unified, user-friendly service.

Complementary Efforts

FEDER is part of a broader ecosystem of data initiatives. MIT's Plasma Science and Fusion Center leads a parallel DOE-funded effort to distribute data from devices like the Alcator C-Mod using platforms that adhere to FAIR principles—Findable, Interoperable, Accessible, Reusable. Their databases will use MDSplusML, an upgraded version of software that nearly 40 fusion research institutes currently use for data storage and access.

Documentation from the International Atomic Energy Agency's 2024 Technical Meeting on Control Systems and Data Management highlights the scope of the challenge, noting that "machine learning applications require data from large numbers of shots and potentially from different devices," emphasizing platforms like FEDER's importance.

Looking Ahead

The success of FEDER could prove crucial for achieving fusion energy's commercial promise. By enabling researchers to "capture each new result and underlying workflow in FEDER's living data commons and make them available for immediate reuse in the next experiment or simulation," the platform could dramatically accelerate fusion development.

Academic analysis supports this potential. Research published in the Journal of Fusion Energy identifies data sharing and AI integration as "Priority Research Opportunities" that could "enable or accelerate progress toward the realization of fusion energy by maximizing the amount and usefulness of information extracted from experimental and simulation output data."

The initiative also addresses what government reviews have identified as a key challenge. The GAO's 2024 assessment noted that "public and private sector misalignments" represent a significant obstacle to fusion development, particularly around research priorities where coordination between basic science and technology development is essential.

As the fusion community moves toward demonstrating commercial viability in the 2030s, platforms like FEDER represent essential infrastructure for success. In the race to bring the power of the stars to Earth, the ability to efficiently share and build upon collective knowledge may prove as important as the plasma physics breakthroughs themselves.

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SIDEBAR: Accessing the FEDER Database

Who Can Access FEDER?

Research Community:

• Scientists and engineers at DOE national laboratories
• Faculty and researchers at participating universities
• International collaborators through existing partnerships (ITER, bilateral agreements)
• Private fusion companies participating in DOE programs (FIRE Collaboratives, Milestone Program)

Access Levels:

• Full Access: Core FEDER consortium members and FIRE Collaborative participants
• Registered Access: Approved researchers at academic institutions and national labs working on fusion-relevant research
• Collaborative Access: International partners through formal agreements (ITER, bilateral partnerships with UK, Japan, and other strategic allies as outlined in DOE's international fusion strategy)
• Industry Access: Private companies participating in DOE programs (FIRE Collaboratives, Milestone-Based Fusion Development Program, INFUSE partnerships)

How to Access FEDER Data

Step 1: Registration

• Submit application through FEDER portal (anticipated launch: late 2025)
• Provide institutional affiliation and research credentials
• Specify intended use and data requirements
• Undergo security and export control review

Step 2: Data Access Protocols

• Secure Authentication: Multi-factor authentication required
• Data Classification: Different access levels for public, sensitive, and export-controlled data
• Usage Agreements: Acceptance of data sharing protocols and attribution requirements
• Audit Trail: All data access and usage tracked for security and accountability

Step 3: Technical Requirements

• Network Access: High-speed internet connection recommended
• Computing Resources: Local analysis capabilities or access to DOE computing centers
• Software Tools: Compatible analysis software (open-source tools prioritized)
• Training: Completion of FEDER platform training modules

Data Sharing Principles

Open Science Approach:

• Unclassified experimental data made publicly available after embargo periods
• Simulation results and analysis workflows shared with proper attribution
• Real-time collaboration tools for multi-institutional projects

Security Considerations:

• Export-controlled data requires additional clearances
• International access subject to U.S. export regulations
• Proprietary industrial data protected through tiered access systems

Contact Information:

• FEDER Help Desk: [To be established]
• Institution Coordinators: Available at each participating organization
• Technical Support: Provided through SDSC and consortium partners

Note: Specific access procedures and requirements will be finalized during FEDER's first year of implementation. Potential users should monitor DOE FIRE Collaborative announcements and contact their institutional representatives for updates.

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Sources

Primary Government Sources:

1. U.S. Department of Energy. "Fusion Energy Strategy 2024." June 2024. https://www.energy.gov/sites/default/files/2024-06/fusion-energy-strategy-2024.pdf
2. U.S. Department of Energy. "DOE Announces New Decadal Fusion Energy Strategy." June 6, 2024. https://www.energy.gov/articles/doe-announces-new-decadal-fusion-energy-strategy
3. U.S. Government Accountability Office. "Fusion Energy: Potentially Transformative Technology Still Faces Fundamental Challenges." GAO-23-105813, March 2023. https://www.gao.gov/products/gao-23-105813
4. U.S. Government Accountability Office. "Fusion Energy: Additional Planning Would Strengthen DOE's Efforts to Facilitate Commercialization." GAO-25-107037, December 2024. https://www.gao.gov/products/gao-25-107037
5. U.S. Department of Energy, Office of Science. "Fusion Innovation Research Engine (FIRE) Collaboratives Program." https://science.osti.gov/fes/Community-Resources/FIRE-Collaboratives

Academic Journal Sources: 5. Journal of Fusion Energy. "Advancing Fusion with Machine Learning Research Needs Workshop Report." September 2020. https://link.springer.com/article/10.1007/s10894-020-00258-1

6. Nuclear Fusion. "Plasma surrogate modelling using Fourier neural operators." Vol. 64, 2024. https://iopscience.iop.org/journal/0029-5515
7. Frontiers in Physics. "AI foundation models for experimental fusion tasks." Vol. 12, February 2025. https://www.frontiersin.org/journals/physics/articles/10.3389/fphy.2024.1531334/full
8. Southern Energy Construction. "Development trends of nuclear fusion energy technology and its industrial chain." Vol. 12(4), 2025. https://www.energychina.press/en/article/doi/10.16516/j.ceec.2024-427

International Organization Sources: 9. International Atomic Energy Agency. "IAEA World Fusion Outlook 2024." November 2024. https://www-pub.iaea.org/MTCD/Publications/PDF/p15777-24-02766E_WFO_web.pdf

10. International Atomic Energy Agency. "Fusion Energy Momentum Highlighted at Ministerial Meeting." November 6, 2024. https://www.iaea.org/newscenter/pressreleases/fusion-energy-momentum-highlighted-at-ministerial-meeting-hosted-by-iaea-and-italy

Research Institution and University Sources: 11. General Atomics. "General Atomics Leads Effort to Build National Data Ecosystem Aimed at Accelerating Commercial Fusion Power." September 16, 2025. https://www.ga.com/general-atomics-leads-effort-to-build-national-data-ecosystem-aimed-at-accelerating-commercial-fusion-power

12. UC San Diego. "UC San Diego Plays Key Role in National Effort to Build a Fusion Research Data Platform." September 16, 2025. https://today.ucsd.edu/story/uc-san-diego-plays-key-role-in-national-effort-to-build-a-fusion-research-data-platform
13. General Atomics. "General Atomics-led Team Awarded $7.4 Million to Develop Transformational Fusion Data Platform." September 1, 2024. https://www.ga.com/ga-led-team-awarded-74-million-to-develop-transformational-fusion-data-platform
14. MIT Climate Portal. "Fast-tracking fusion energy's arrival with AI and accessibility." https://climate.mit.edu/posts/fast-tracking-fusion-energys-arrival-ai-and-accessibility
15. Princeton Engineering. "Engineers use AI to wrangle fusion power for the grid." February 21, 2024. https://engineering.princeton.edu/news/2024/02/21/engineers-use-ai-wrangle-fusion-power-grid
16. American Nuclear Society. "Fusion fired up? Milestones met and six FIRE collaboratives named." January 22, 2025. https://www.ans.org/news/2025-01-22/article-6698/fusion-fired-up-milestones-met-and-six-fire-collaboratives-named/
17. UC San Diego Plays Key Role in National Effort to Build a Fusion Research Data Platform