How AI Can Unlock Fusion Energy

By: Jeff Schneider

Combining generative AI with reinforcement learning can help the U.S. win the race to fusion energy by finding better ways to reach the plasma temperatures and pressures needed for fusion power plants. Researchers at Carnegie Mellon University are using these advances in AI to unlock the promise of unlimited, clean energy from fusion.

Why it matters: Consider some of the world's grand challenges: producing enough food, the availability of clean water, or handling climate change. These are all primarily energy problems. We already have the ability to produce and deliver food and water – it’s just energy-intensive, and thus expensive to do so. The biggest contributor to climate change is our inability to generate sufficient energy without unwanted side effects.

Nuclear fusion, the reaction that powers the sun and stars, holds the promise of solving these problems here on Earth. 

• There is a nearly unlimited supply of fuel, some of which can be derived from seawater.
• It does not produce long-term harmful byproducts. Its power plants can not melt down.
• The biggest hurdle to delivering on this promise is our inability to sustain the extremely high plasma temperatures and pressures needed for a financially viable power plant. Solving that challenge is where AI comes in.

The big question: The traditional research approach to solving hard scientific and engineering challenges like nuclear fusion centers on individual scientists. It is the single-scientist, single-hypothesis, single-experiment, single-data set analysis, single conclusion approach to science. That has worked in the past, but it is too slow.

Now, AI algorithms, working alongside scientists, can reason about all the data, all the hypotheses, all the accumulated scientific knowledge, and design experiments at a much faster rate than individual scientists alone. The question we ask is how to design those AI algorithms, and what is the collaborative discovery process that delivers those breakthroughs for nuclear fusion.

Catch up quick: There are two recent developments, and one that is still missing, that enable this new approach to science for nuclear fusion. 

• Advances in large language models: They now provide that repository of scientific knowledge that previously wasn't very accessible to other AI algorithms.
• Advances in reinforcement learning and discovery algorithms: They allow for work at the scale needed for hard problems like nuclear fusion.  

Finally, the missing development is the national capacity to generate more data through experiments on our research fusion devices. These devices  were built and funded to generate enough data to support the old single-scientist model, which couldn't handle much data anyway. AI can handle more data and needs it to make these big advances.

What we’re doing: With funding from the Department of Energy, the Auton Lab at CMU, a collaboration with the Princeton Plasma Physics Lab, and DIII-D at General Atomics, has demonstrated the ability of AI to: do closed loop control of temperature and density profiles; optimize heating profiles for stability, stably reach high pressures, and do so at multiple fusion devices.

Policy takeaways: In order for the U.S. to fully leverage the advances in nuclear fusion made possible with AI, policy makers should reconsider the national capability to run fusion experiments and dramatically expand the operation to run more experiments and thus obtain more data for AI.

• In the near term, this means the Department of Energy buying more experiment time on the devices we already have and allocating it to AI-guided data collection.  
• In the longer term, it means building new devices designed for high throughput experimentation.

The U.S. needs a national AI for Fusion Initiative to support model development, empower the national labs to expand access and develop a lab to manufacturing strategy for devices.