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"In collaboration with the Swiss Plasma Center at EPFL—a university in Lausanne, Switzerland—the UK-based AI firm has now trained a deep reinforcement learning algorithm to control the superheated soup of matter inside a nuclear fusion reactor. The breakthrough, published in the journal Nature, could help physicists better understand how fusion works, and potentially speed up the arrival of an unlimited source of clean energy...

Controlling nuclear fusion on Earth is hard, however. The problem is that atomic nuclei repel each other. Smashing them together inside a reactor can only be done at extremely high temperatures, often reaching hundreds of millions of degrees—hotter than the center of the sun. At these temperatures, matter is neither solid, liquid, nor gas. It enters a fourth state, known as plasma: a roiling, superheated soup of particles...

Controlling the plasma requires constant monitoring and manipulation of the magnetic field. The team trained its reinforcement-learning algorithm to do this inside a simulation. Once it had learned how to control—and change—the shape of the plasma inside a virtual reactor, the researchers gave it control of the magnets in the Variable Configuration Tokamak (TCV), an experimental reactor in Lausanne. They found that the AI was able to control the real reactor without any additional fine-tuning. In total, the AI controlled the plasma for only two seconds—but this is as long as the TCV reactor can run before getting too hot...

The researchers believe that using AI to control plasma will make it easier to experiment with different conditions inside reactors, helping them understand the process and potentially speeding up the development of commercial nuclear fusion. The AI also learned how to control the plasma by adjusting magnets in a way that humans had not tried before, which suggests that there may be new reactor configurations to explore."

DeepMind’s AI can control superheated plasma inside a fusion reactor The prospect of unlimited clean energy is still a long way off, but this is another example of DeepMind tackling hard real-world problems.