Tech Insights

Is Fusion’s Future Powered by AI?

June 07, 2024 by Kevin Clemens

Achieving nuclear fusion has been challenging, but advancements in artificial intelligence could offer some hope.

For decades, scientists have dreamt of a world powered by nuclear fusion, the same process fueling the sun. This almost limitless, clean energy source could transform society, providing a solution to growing energy needs without the environmental drawbacks and climate warming emissions of fossil fuels. 

Despite significant advances, nuclear fusion has remained elusive. Yet, developments in artificial intelligence (AI) are showing promise to finally bring this dream within reach.


Inside a tokamak

Inside a tokamak. Image used courtesy of the DIII-D National Fusion Facility 


Challenges and Advances in Nuclear Fusion

Nuclear fusion involves combining light atomic nuclei, such as hydrogen, to form heavier nuclei, releasing vast amounts of energy in the process. This contrasts with nuclear fission, the method used in nuclear power plants, which splits apart heavy atomic nuclei such as uranium. Fusion promises several advantages over fission: it produces very little radioactive waste, the fuel (hydrogen isotopes such as tritium and deuterium) is abundant, and there is no risk of a runaway reaction that can result in a meltdown.

Despite these advantages, achieving controlled nuclear fusion on Earth has proven extraordinarily challenging. The conditions necessary for fusion—extremely high temperatures and pressures similar to those in the Sun—are difficult to create and maintain on Earth. Scientists have experimented with various approaches, such as magnetic confinement (used in tokamaks) and inertial confinement (used in laser-based systems). However, sustainable and practical fusion power has remained out of reach.


How fusion works within a tokamak.

How fusion works within a tokamak. Image used courtesy of the Department of Energy


Progress is advancing, however. On December 13, 2022, scientists at Lawrence Livermore National Laboratory’s National Ignition Facility (NIF) in California achieved breakeven for the first time in history with a laser-powered fusion reaction. The process used almost 200 ultraviolet laser beams to deliver energy, about 2.05 million joules (MJ), to a deuterium-tritium fuel pellet. The resulting nuclear fusion reaction achieved an output of 3.15 MJ, about the energy contained in three sticks of dynamite, thus achieving breakeven.


Artificial Intelligence in Optimizing Reactor Designs

Despite dystopian fears that artificial intelligence (A) will someday destroy the human race, it is a powerful tool in applications, including weather predictions, pollution tracking, deforestation mapping, and ice-melting monitoring. In the last few years, AI has also emerged as a useful tool in nuclear fusion research, offering new ways to tackle some of the field's biggest challenges. 

One significant hurdle in fusion research is designing reactors to achieve and sustain the conditions necessary for fusion. Traditional methods rely on complex mathematical models and simulations, which can be incredibly time-consuming and computationally intensive. AI, particularly machine learning (ML) algorithms, can drastically reduce the time required for these simulations.

For example, researchers at the Princeton Plasma Physics Laboratory (PPPL) have developed ML algorithms to predict different reactor design outcomes with remarkable accuracy. By training these algorithms on vast datasets from previous experiments and simulations, scientists can quickly identify the most promising designs and optimize them in ways that would be impossible through conventional methods.


Controlling Plasma Behavior

Plasma, the hot, ionized gas where fusion reactions occur, is notoriously difficult to control. It tends to be highly unstable, with a propensity for turbulent behavior that can quench fusion reactions. This tearing of the magnetic fields containing the reaction allows the plasma to escape, ending the fusion reaction. 


The formation of a tearing instability (left) results in a plasma disruption, ending the fusion reaction.

The formation of a tearing instability (left) results in a plasma disruption, ending the fusion reaction. Image used courtesy of PPPL


The Princeton PPPL team uses vast amounts of data collected from past experiments at the DIII-D tokamak in San Diego to construct a deep neural network capable of predicting the potential for future tearing instability based on real-time plasma characteristics. The algorithm works within a simulated environment and tries various strategies to control parameters to contain the fusion reaction. The PPPL researchers’ system has demonstrated that it can forecast potential plasma tearing instabilities up to 300 milliseconds in advance. An AI-powered controller has plenty of time to change parameters and avoid a tear within the plasma’s magnetic field lines.


Data-Driven Insights

Fusion experiments generate vast amounts of data, much of which goes unanalyzed due to its sheer volume and complexity. AI excels at sifting through large datasets, identifying patterns and correlations human researchers might miss. This capability can be harnessed to uncover insights into plasma physics and fusion dynamics.

For instance, the ITER project, an international collaboration building France's world's largest tokamak, is leveraging AI to analyze data from previous fusion experiments worldwide. By integrating data from different sources, AI can identify trends and anomalies, providing researchers with a deeper understanding of the conditions necessary for successful fusion.


Accelerating Material Science

The materials used in fusion reactors must withstand extreme conditions—intense heat, radiation, and mechanical stress. Developing new materials that can endure these conditions is critical to fusion research. AI plays a crucial role here, particularly through techniques like generative design and predictive modeling.

Los Alamos National Laboratory researchers are using AI to accelerate the discovery of new materials. By training AI models on data from materials science, they can predict the properties of new compounds and determine the most promising candidates for fusion applications. This approach significantly speeds up the materials discovery process, potentially leading to breakthroughs in the development of reactor components.


AI Ahead

While AI provides valuable tools and insights, practical nuclear fusion still faces innumerable challenges. Significant technical hurdles remain, and translating laboratory successes into scalable, commercial fusion power will require sustained effort and investments estimated to be more than a trillion dollars. However, integrating AI into fusion research is a game-changer, offering new hope that the dream of fusion energy might finally become a reality.