Mastering Fusion with AI to Unlock Infinite Clean Energy

With fossil fuels fast depleting and climate change worsening, we are in critical need for clean energy. 

While renewable energy sources offer a promising alternative, they have their own challenges in terms of energy storage limitations, grid integration complexities, high initial costs, and the intermittent nature of some sources like solar and wind. Additionally, land use, policy barriers, and the need for robust infrastructure also pose significant hurdles. 

This is where nuclear energy can change the game. Nuclear energy is a form of energy released from the nucleus, which is the positively charged central core of atoms, consisting of protons and neutrons. 

According to the International Energy Agency (IEA), nuclear power accounts for about 10% of electricity generation globally. 

But despite its ability to produce emissions-free power, large up-front costs, long lead times, and a poor record of on-time delivery are limiting nuclear energy’s adoption, noted the IEA. However, fresh momentum around the world has the potential to open a new era for nuclear energy with reactors to begin commercial operations in China, Europe, India, Japan, and Korea.

Now, there are two ways this source of energy can be produced:

• Fusion
• Fission

Fission is where the nuclei of heavy atoms are split into smaller ones. Fusion is where the nuclei of lighter atoms are fused together to form a heavier one. Both release tremendous amounts of energy.

While only fission is currently used on a large scale for electricity generation, today, our focus is on nuclear fusion.

Still in the research and development phase, nuclear fusion contributes very little to global energy production, though it holds immense potential for the future. It is actually among the most environmentally friendly sources of energy, as there are no carbon dioxide or other harmful emissions during the process.

Zero-carbon electricity from fusion power plants can also flow continuously, no matter the weather or whether it’s day or night. Unlike fission, fusion doesn’t even create any potentially disastrous meltdowns or long-lasting radioactive nuclear waste.

Not only does fusion not contribute to greenhouse gas (GHG) emissions, its two main sources of fuel, hydrogen (for deuterium) and lithium (for tritium), are also widely available.

Against this backdrop, nuclear fusion is expected to meet humanity’s energy needs for millions of years, and that has countries around the world actively working on making it a reality. 

Obstacles Between Nuclear Energy Vision and Reality

It was in 1915 that the concept of nuclear fusion was first proposed by American chemist William Draper Harkins. But we have yet to achieve it meaningfully. 

Fusion reactions actually make up the fundamental energy source of stars, including the giant ball of hot gas, the Sun. It is the process called thermonuclear fusion that has kept the Sun and the stars shining for millions of years.

In thermonuclear fusion, atomic nuclei combine at extremely high temperatures, ten to a hundred million degrees Centigrade, which provides enough energy to overcome the mutual electrical repulsion of two nuclei. They then fuse to form a single nucleus and release significant energy.

The Sun’s massive gravitational force plays a key role here, as it produces extreme pressure, heat, and the conditions for fusion. Without gravity, the sun would not be hot and dense enough for fusion to happen. 

Now, to create nuclear fusion on Earth but without crushing levels of gravity means putting light isotopes into a reactor and then heating them to hundreds of millions of degrees Celsius, which turns them into an ionized ‘plasma.’

Plasma is a charged gas made of positive ions and free-moving electrons. It is extremely hot and difficult to control, requiring a magnetic field to prevent it from escaping.

Scientists have been able to routinely achieve conditions for nuclear fusion, but plasma stability and improved confinement properties are yet to be attained to maintain the reaction and produce energy in a sustained manner.

For instance, in late 2022, a multibillion-dollar fusion experiment finally got a tiny isotope sample to release more energy than the laser energy used to ignite it, but it lasted only about one-tenth of a nanosecond.

So, nuclear fusion is a very challenging process, requiring extreme conditions of high temperatures and intense pressure.

Then, there’s the need to stabilize the plasma, prevent it from touching the reactor walls, and minimize heat loss, making it difficult to sustain the reaction. In terms of engineering, we need large, powerful superconducting magnets to confine the plasma in tokamaks, a common type of doughnut-shaped fusion reactor, and advanced vacuum systems to achieve and maintain the extremely low pressures.

Additionally, we need materials that can withstand high temperatures, high heat fluxes, and intense neutron radiation. To address these challenges, scientists are turning to artificial intelligence (AI). 

AI is playing an increasingly vital role in accelerating nuclear fusion research and development. By leveraging machine learning and other AI algorithms, researchers are optimizing reactor design, finding and correcting fundamental measurement errors, accelerating materials discovery, predicting and preventing plasma disruptions, controlling the plasma state, and improving the efficiency and stability of fusion reactions.

By handling vast amounts of complex data and the intricate relationship between different facets of the fusion process, AI can further improve our understanding of it, accelerate the development of new reactor designs, and significantly reduce development timelines, paving the way for nuclear energy’s commercialization.

AI Helps Overcome the Physics Barriers of Fusion

AI is rapidly transforming industries, including the energy sector, where it is helping solve the global energy crisis by achieving carbon-free, limitless nuclear energy. 

In regard to that, Google DeepMind utilized deep reinforcement learning (RL) to successfully control the plasma¹ in a tokamak and precisely sculpt it into different shapes. To develop the RL system, they collaborated with the Swiss Plasma Center at EPFL. The system autonomously discovers how to control the magnetic coils surrounding a tokamak and contain the plasma in it.

Last year, the company also released a plasma simulator called TORAX², which models the “core” of the plasma and then forecasts changes in temperature, density, and electric current. 

Earlier last year, a team at Princeton University also used RL to predict the leading form of disturbances in fusion plasma up to 300 milliseconds before they appear. Known as “tearing mode instabilities,” it occurs when the magnetic field lines within a plasma break, allowing it to escape and thus stopping the fusion process. 

These instabilities are expected to “become even more prominent as we try to run fusion reactions at the high powers required to produce enough energy,” said first author Jaemin Seo, an assistant professor of physics at South Korea’s Chung-Ang University. “They are an important challenge for us to solve.”

Their AI model forecasts potential tearing mode instabilities in advance³ and then makes changes to certain operating parameters to avoid the tearing within the plasma’s magnetic field lines.

The model, trained on past experimental data, is given a goal to maintain a high-powered reaction, along with conditions to avoid, such as tearing mode instability, and the dials it can turn to achieve these outcomes. Over time, the AI model “learns the optimal pathway” to sustain the high-power goal while avoiding instability.

“By learning from past experiments, rather than incorporating information from physics-based models, the AI could develop a final control policy that supported a stable, high-powered plasma regime in real time, at a real reactor.”

– Research leader Egemen Kolemen

While the model ran countless simulated fusion experiments to find pathways, in the background, the team observed its “intentions” and refined actions, acting as an arbitrator “between what the AI wants to do and what the tokamak can accommodate.”

The team of engineers, physicists, and data scientists tested and demonstrated their model in experiments at the DIII-D National Fusion Facility in San Diego.

Then, earlier this year, a research paper titled “Prediction of Performance and Turbulence in ITER Burning Plasmas via Nonlinear Gyrokinetic Profile Prediction“⁴ was published by Nathan Howard, a principal research scientist at the MIT Plasma Science and Fusion Center (PSFC).

Howard and his team used ML and simulations to predict how plasma will behave in a fusion device. 

“In the paper, they explained that high-resolution simulations of turbulence confirm that the ITER tokamak, the world’s largest experimental fusion device currently under construction in southern France, will perform as expected when it begins operation, which will not be until at least 2035.

To verify the scenario, the team used CGYRO, a time-intensive program that applies a complex plasma physics model to certain operating conditions in order to produce detailed simulations on plasma behavior in different locations within a fusion device.

The simulations were then run through the PORTALS framework, which takes the high-fidelity runs and uses ML to build a ‘surrogate,’ a model that “can mimic the results of the more complex runs, but much faster.”

High-fidelity modeling tools like PORTALS, the researchers noted, provide “a glimpse into the plasma core before it even forms. This predict-first approach allows us to create more efficient plasmas in a device like ITER.”

The team also demonstrated different operating setups where the same amount of energy output can be produced but with less energy input.

Swipe to scroll →

Mapping the Fusion Shadows with HEAT-ML 

A new AI approach is now being used to protect the insides of fusion reactors from the extreme heat of plasma. This new way is accelerating the speed of calculations required to find “magnetic shadows” in the fusion containers.

These shadows are safe havens that are protected from the intense plasma heat.

The Heat Challenge Inside the Tokamak

As we explained above, when the plasma is confined in the tokamak using magnetic fields, the heat that comes from the plasma reaches a temperature that is even hotter than the sun’s core. In order to harness fusion, this heat needs to be controlled.

According to Doménica Corona Rivera, an associate research physicist at Princeton Plasma Physics Laboratory (PPPL):

“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. The worst thing that can happen is that you would have to stop operations.”

So, to overcome this problem, the researchers are utilizing AI that can speed up the calculations that predict just where the heat will hit in the tokamak.

By significantly accelerating HEAT computations, the model enables the possibility of real-time applications for divertor protection and control actions.

A Collaboration to Drive Fusion Innovation

A public-private partnership between PPL, the U.S. Department of Energy’s (DOE) Oak Ridge National Laboratory, and the fusion power company Commonwealth Fusion Systems (CFS), which is a spin-out from MIT and aims to build a small fusion power plant based on the ARC tokamak design, led to this new AI approach called HEAT-ML.

The ML-based surrogate model (HEAT-ML) is detailed in the latest study titled “Shadow masks predictions in SPARC tokamak plasma-facing components using HEAT code and machine learning methods.”⁵

HEAT-ML aims to lay down the foundation for software to accelerate the design of future fusion systems, as well as prevent any problems before they happen by adjusting the plasma, enabling good decision-making.

“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.”

– Paper’s co-author, Michael Churchill, who’s the head of digital engineering at PPPL.

Mapping Magnetic Shadows to Protect Materials

HEAT-ML has been specifically made to simulate a small part of SPARC.

SPARC is a tokamak that is currently under development by CFS in collaboration with the MIT PSFC. It is scheduled to begin operations next year and aims to demonstrate net energy gain, generating more energy than it consumes by 2027.

In order to achieve that, the researchers are simulating just how heat impacts the interior of this tokamak.

A big computing challenge, the researchers make it manageable by focusing on the SPARC section, where the severity of plasma heat exhaust meets the material wall. And that’s 15 tiles right around the base of the machine, which is the part where its exhaust system will be vulnerable to most heat.

To create this simulation, the team created shadow masks, which are 3D maps of magnetic shadows. These maps are specific areas on the surfaces of a fusion system’s internal components that are protected from direct heat. Their position depends on the shape of the parts inside the tokamak as well as how they interact with the magnetic field lines confining the plasma.

Speeding up Simulations From Minutes to Milliseconds

The shadow masks were originally calculated using the open-source computer program called HEAT, or the Heat flux Engineering Analysis Toolkit.

Built by Tom Looby from CFS in collaboration with SPARC Diagnostic Team’s leader Matt Reinke, this program was initially applied to the exhaust system for PPPL’s spherical tokamak called the National Spherical Torus Experiment-Upgrade machine (NSTX-U), which is designed to be the most powerful in the world.

The researchers have now used machine learning to supplement HEAT, developing 3D surrogate models for quick and precise calculations of heat load.

The resulting HEAT-ML follows the magnetic field lines from a component’s surface to see if they intersect other internal parts. If the line does intersect, that spot is marked as a shadowed region, or a magnetic shadow.

This whole process of tracing lines and detecting just where they meet the detailed 3D machine geometry, however, was a significant bottleneck that could take about 30 minutes for just one simulation. If there are complex geometries involved, it can take even longer.

HEAT-ML enabled the team to overcome this restriction, speeding up the calculations to mere milliseconds. 

Toward Real-time Control of Fusion Power Plants

The HEAT-ML model uses a deep neural network, a form of AI with multiple hidden layers applied to data to learn specific tasks by recognizing patterns. In this case, HEAT-ML was trained on about 1,000 SPARC simulations from HEAT to identify shadow regions.

Because it is linked to the specific design of SPARC’s exhaust system, HEAT-ML only works for that part of this particular tokamak.

So, currently, it is an optional setting in the HEAT code. But the team hopes to expand their model’s capabilities to generalize the shadow mask calculations for any exhaust systems, regardless of their size and shape, as well as other plasma-facing components inside the tokamak.

The power exhaust, according to the study, is a crucial challenge for the next generation of fusion devices and needs innovative solutions in divertor design and operation. The study noted:

“The ultimate goal is to integrate the model for real-time control and future operational decisions.” 

Investing in Nuclear Energy

The tech giant Microsoft Corporation (MSFT +0.7%) is actively exploring nuclear energy and its acceleration via AI, as it believes that it should be part of a mix of carbon-free energy sources. 

Microsoft’s chief sustainability officer, Melanie Nakagawa, has called fusion a longer-term bet and said that over the last few years, different types of milestones have been hit by the industry that has created “a lot of optimism that this could be the moment that fusion actually comes forward within this decade, or near in this decade.”

Microsoft Corporation (MSFT +0.7%)

For its fusion goals, Microsoft signed a groundbreaking deal with the private fusion startup Helion Energy back in 2023. As per the deal, Helion will supply fusion-generated power to Microsoft data centers by 2028.

Also backed by SoftBank’s venture capital arm and OpenAI’s Sam Altman, Helion began construction on its planned nuclear fusion power plant called Orion this year, though final permits are yet to be secured. 

Helion’s prototype Polaris is currently working on finding a way to generate more energy than what it takes to create and sustain the reaction. Orion, meanwhile, according to its CEO, will connect to power delivery networks.

When it comes to Microsoft’s market performance, the $3.86 trillion market cap company’s shares are trading around $521, up 23.41% YTD. It has an EPS (TTM) of 13.64 and a P/E (TTM) of 38.25. The dividend yield available is 0.64%.

As for financials for the quarter ended June 30, 2025, it revealed an 18% increase in revenue to $76.4 bln, a 23% jump in operating income to $34.3 bln, and a 24% rise in net income to $27.2 bl. Diluted earnings per share, meanwhile, were $3.65, up 24%.

“Cloud and AI are the driving force of business transformation across every industry and sector.”

– CEO Satya Nadella

Latest Microsoft Corporation (MSFT) Stock News and Developments

Conclusion

One of the most promising pathways to clean energy is fusion, but the road to its commercialization is long and arduous. However, AI is proving to be the accelerant that can finally turn this centuries-old dream into reality, well within our lifetimes.

By utilizing AI-based approaches, researchers are working on making fusion systems robust and economically viable. From speeding up research and solving plasma instabilities to protecting reactor systems, AI can help bring nuclear energy to the grid and allow humanity to access limitless power.

Click here to learn what the fourth generation of nuclear energy would look like.

References:

1. Degrave, J., Felici, F., Buchli, J., et al. Magnetic control of tokamak plasmas through deep reinforcement learning. Nature, 602(7897), 414–419, published 16 February 2022. https://doi.org/10.1038/s41586-021-04301-9
2. Citrin, J., Goodfellow, I., Raju, A., Chen, J., Degrave, J., Donner, C., Felici, F., Hamel, P., Huber, A., Nikulin, D., Pfau, D., Tracey, B., Riedmiller, M., & Kohli, P. TORAX: A fast and differentiable tokamak transport simulator in JAX. arXiv preprint arXiv:2406.06718, published 10 June 2024, last revised 7 December 2024. https://doi.org/10.48550/arXiv.2406.06718
3. Seo, J., Kim, S., Jalalvand, A., et al. Avoiding fusion plasma tearing instability with deep reinforcement learning. Nature, 626(8001), 746–751, published 21 February 2024. https://doi.org/10.1038/s41586-024-07024-9
4. Howard, N. T., Rodriguez-Fernandez, P., Holland, C., & Candy, J. Prediction of performance and turbulence in ITER burning plasmas via nonlinear gyrokinetic profile prediction. Nuclear Fusion, 65(1), 016002, published 11 November 2024. https://doi.org/10.1088/1741-4326/ad8804
5. Corona, D., Scotto d’Abusco, M., Churchill, M., Munaretto, S., Kleiner, A., Wingen, A., & Looby, T. Shadow masks predictions in SPARC tokamak plasma-facing components using HEAT code and machine learning methods. Fusion Engineering and Design, 217, 115010, published August 2025. https://doi.org/10.1016/j.fusengdes.2025.115010