A new artificial intelligence breakthrough is helping scientists tame the extreme heat of fusion plasma, bringing the dream of limitless clean energy one step closer.

A public-private team of fusion pioneers – Commonwealth Fusion Systems (CFS), the U.S. Department of Energy’s Princeton Plasma Physics Laboratory (PPPL), and Oak Ridge National Laboratory – has unveiled an AI breakthrough that could reshape the future of fusion plasma research.

The new system, called HEAT-ML, can identify safe zones inside a reactor in milliseconds, replacing a process that once took more than 30 minutes.

By protecting sensitive components from the blistering heat of superheated plasma, this advance could accelerate the design and operation of next-generation fusion power plants.

The heat challenge inside a fusion vessel

Fusion, the same process that powers the Sun, has long been seen as a pathway to virtually limitless clean electricity.

In a fusion reactor, hydrogen atoms fuse under extreme temperatures and pressures, releasing massive amounts of energy.

But inside a tokamak – a doughnut-shaped vessel that uses magnetic fields to confine the fusion plasma – temperatures can exceed those at the Sun’s core.

At these extremes, even high-tech reactor walls can melt or degrade if exposed to concentrated heat streams. To prevent damage, engineers identify ‘magnetic shadows’ – areas shielded from direct plasma heat by other parts of the machine.

These zones are critical for determining where heat-resistant materials should go and how to adjust plasma conditions to avoid harmful hotspots.

From hours to milliseconds

Traditionally, magnetic shadow mapping relied on an open-source tool called the Heat flux Engineering Analysis Toolkit (HEAT).

HEAT calculates ‘shadow masks’ — 3D maps showing which parts of the reactor interior are protected – by simulating how magnetic field lines interact with the machine’s components.

While accurate, HEAT’s detailed tracing of magnetic lines through complex reactor geometries could take up to 30 minutes for a single simulation, and far longer for intricate designs. This posed a major bottleneck for projects like CFS’s SPARC tokamak, which aims to achieve net energy gain by 2027.

HEAT-ML eliminates this bottleneck. Using a deep neural network trained on roughly 1,000 HEAT simulations, the AI can predict magnetic shadow locations in just milliseconds –  a speedup of several orders of magnitude.

This leap means designers can run vastly more simulations in less time, enabling faster optimisation and real-time operational adjustments.

Focus on SPARC’s host heat-intense region

The initial version of HEAT-ML focuses on a small but critical section of SPARC’s exhaust system, specifically, 15 tiles at the machine’s base where fusion plasma heat will be most intense.

By predicting shadowed areas here, engineers can plan the layout of heat-resistant components, extending their lifespan and reducing the risk of emergency shutdowns.

These simulations are not just for pre-construction planning. Once operational, the system could guide real-time decisions, tweaking magnetic configurations during experiments to divert damaging heat away from vulnerable surfaces.

From specialised tool to universal application

While HEAT-ML is currently tailored to SPARC’s exhaust geometry, the research team envisions expanding it to handle any part of any tokamak.

In the future, a generalised version could map magnetic shadows for all plasma-facing components, from exhaust systems to inner walls, regardless of shape or size.

Such versatility would be invaluable as fusion research moves toward commercial power plants, where downtime from component damage could mean significant operational and financial losses.

Powering the future with fusion

As the race to harness fusion plasma intensifies, breakthroughs like HEAT-ML are crucial.

The ability to run heat-impact simulations in milliseconds instead of minutes opens the door to faster design cycles, more flexible operation, and greater protection for the expensive materials inside a fusion reactor.

If expanded beyond SPARC, HEAT-ML could become a standard tool for designing and running fusion plants worldwide.