AI Accelerates Fusion Energy Research by Pinpointing Reactor ‘Safe Zones’
The quest for sustainable fusion energy – harnessing the power of the stars here on Earth – took a significant leap forward recently with the development of a new artificial intelligence (AI) approach. This innovation, a collaboration between Commonwealth Fusion Systems (CFS), the U.S. Department of Energy’s (DOE) Princeton Plasma Physics Laboratory (PPPL), and Oak Ridge National Laboratory, dramatically speeds up the identification of “magnetic shadows” within a fusion reactor. These shadows represent critical safe havens, shielding the vessel’s components from the extreme heat generated by the plasma, which can reach temperatures exceeding those found at the sun’s core. The ability to quickly and accurately map these areas is crucial for designing and operating future fusion power plants, bringing the promise of clean, limitless energy closer to reality.
Known as HEAT-ML, this AI system isn’t intended to replace existing fusion research methods, but rather to augment them. It acts as a powerful surrogate model, accelerating calculations that previously took significant time and computational resources. This breakthrough could revolutionize the design process, allowing engineers to explore a wider range of configurations and optimize reactor performance more efficiently. HEAT-ML has the potential to enable real-time adjustments during fusion operations, proactively mitigating potential problems before they escalate. The development underscores the growing role of AI in tackling some of the most complex scientific and engineering challenges facing humanity.
Fusion energy relies on recreating the conditions found within stars, fusing light atomic nuclei to release tremendous amounts of energy. A key challenge in achieving this on Earth is containing the incredibly hot plasma – a superheated state of matter – using powerful magnetic fields within a device called a tokamak. The intense heat generated by the plasma poses a significant threat to the tokamak’s internal components, necessitating precise control and careful design to prevent damage. Identifying and maximizing the areas shielded from direct heat exposure – the magnetic shadows – is therefore paramount. “The plasma-facing components of the tokamak might come in contact with the plasma, which is very hot and can melt or damage these elements,” explained Doménica Corona Rivera, an associate research physicist at PPPL and first author on the paper detailing HEAT-ML. “The worst thing that can happen is that you would have to stop operations.”
From HEAT to HEAT-ML: A Speed Revolution
The foundation for HEAT-ML lies in an existing open-source computer program called HEAT, or the Heat flux Engineering Analysis Toolkit. Developed by Tom Looby, a CFS Manager, during his doctoral work with Matt Reinke, now leader of the SPARC Diagnostic Team, HEAT was initially applied to the exhaust system of PPPL’s National Spherical Torus Experiment-Upgrade (NSTX-U) machine. HEAT calculates these crucial shadow masks – 3D maps illustrating areas protected from direct heat – by tracing magnetic field lines. However, this process, while effective, was computationally intensive, often requiring around 30 minutes for a single simulation, and even longer for complex geometries.
HEAT-ML overcomes this bottleneck by leveraging the power of deep learning. The AI system traces magnetic field lines to determine if they intersect with internal parts of the tokamak. If an intersection occurs, the region is designated as “shadowed.” The key innovation is the dramatic reduction in calculation time – from approximately 30 minutes to just a few milliseconds. This speedup is achieved through a deep neural network, a type of AI trained on a database of roughly 1,000 simulations generated by the original HEAT program. By learning from this extensive dataset, HEAT-ML can accurately predict shadow masks with unprecedented efficiency. “This research shows that you can take an existing code and create an AI surrogate that will speed up your ability to get useful answers, and it opens up interesting avenues in terms of control and scenario planning,” said Michael Churchill, co-author of the research and head of digital engineering at PPPL.
SPARC and the Path to Net Energy Gain
The initial application of HEAT-ML has focused on simulating a specific section of SPARC, a tokamak currently under construction by Commonwealth Fusion Systems (CFS) in Devens, Massachusetts. CFS aims to demonstrate net energy gain with SPARC by 2027 – a landmark achievement where the reactor produces more energy than it consumes. This ambitious goal requires precise control over the plasma and a thorough understanding of heat distribution within the vessel. The team focused on a particularly challenging area: the section of SPARC’s exhaust system where the most intense plasma heat interacts with the material wall, specifically 15 tiles near the bottom of the machine.
Simulating heat impact on SPARC is a significant computational undertaking. The team’s targeted approach, focusing on this critical exhaust section, allowed them to effectively demonstrate the capabilities of HEAT-ML. The success of this initial implementation paves the way for broader applications of the AI system. Currently, HEAT-ML is tailored to the specific design of SPARC’s exhaust system and functions as an optional setting within the HEAT code. However, researchers are actively working to generalize its capabilities, aiming to create a tool that can calculate shadow masks for exhaust systems of any shape and size, as well as for other plasma-facing components within a tokamak.
Public-Private Partnerships Fuel Innovation
The development of HEAT-ML exemplifies the power of collaboration between public and private entities in accelerating scientific progress. The project involved a partnership between CFS, PPPL, and Oak Ridge National Laboratory, leveraging the expertise and resources of each institution. This collaborative approach is further strengthened by funding from the U.S. Department of Energy (DOE), including support through contracts DE-AC02-09CH11466 and DE-AC05-00OR22725, and direct investment from CFS. The DOE’s Innovation Network for Fusion Energy (INFUSE) program awarded CFS three grants in August 2023 to support research and development projects with the University of California at Berkeley, PPPL, and the University of California at Los Angeles.
This partnership extends beyond research institutions. PPPL is too collaborating with venture capital firm SOSV HAX and the New Jersey Economic Development Authority to create the NJ HAX Plasma Forge, fostering innovation and entrepreneurship in the fusion energy sector. U.S. Secretary of Energy Chris Wright visited PPPL in August 2025 to learn about these collaborations firsthand, highlighting the importance of fusion energy to the nation’s scientific and economic future.
Looking Ahead: The Future of Fusion Energy
While HEAT-ML represents a significant advancement, the journey towards commercially viable fusion energy is ongoing. Researchers continue to address numerous scientific and engineering challenges, including materials science, plasma control, and reactor design. The ability to rapidly and accurately simulate heat distribution within fusion reactors, as enabled by HEAT-ML, is a crucial step in overcoming these hurdles. The next major milestone for CFS is the anticipated demonstration of net energy gain with SPARC by 2027. Success in this endeavor would mark a pivotal moment in the pursuit of clean, sustainable energy for the world.
The continued development and refinement of AI-powered tools like HEAT-ML will undoubtedly play an increasingly critical role in accelerating this progress. As these technologies mature, they will empower researchers and engineers to design, build, and operate fusion reactors with greater efficiency and precision, bringing the dream of fusion energy closer to reality. The ongoing collaboration between public and private sectors, coupled with sustained investment in research and development, will be essential to unlocking the full potential of this transformative energy source.
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