DeepMind applies AI to controlling nuclear fusion reactors Posted on : Feb 17 - 2022

DeepMind, the AI lab backed by Google parent company Alphabet, today announced that it used AI to successfully control superheated matter inside a nuclear fusion reactor. The lab claims that the system, which is detailed in a paper published in the journal Nature, could allow scientists to investigate how such matter reacts under different conditions.

While DeepMind remains engaged in prestige projects like systems that can beat champions at StarCraft II and Go, the lab has in recent years turned its attention to more practical domains, such as code generation, language processing, weather forecasting, app recommendations, and video compression. DeepMind licenses many of its innovations to other Alphabet-owned businesses, like autonomous car company Waymo and YouTube, and it recently launched a spinoff outfit — Isomorphic Labs — focused on drug discovery.

“While there is still much work to be done … we are pleased that the results indicate the power of AI to accelerate and assist fusion science, most likely augmenting human expertise in the field and serving as a tool to discover new and creative approaches for [fusion reactor control] and beyond,” Martin Riedmiller, a research scientist at DeepMind, said during a press briefing this week. “[The work] also suggests that there might be potential for wider adoption of deep reinforcement learning on physical systems for complex scientific and industrial machines, from simple motor control to complex robots.”

Nuclear fusion

Nuclear fusion — the reaction that powers stars, including the Sun — promises clean, limitless energy by smashing and fusing hydrogen atoms into helium. Unlike some energy sources, fusion produces no greenhouse gases and only small amounts of radioactive waste. But at the lower pressures possible on Earth, the temperatures to achieve fusion must be very high, typically over 100 million Celsius.

One solution is the tokamak, a doughnut-shaped vacuum surrounded by magnetic coils that can contain a plasma of hydrogen hotter than the core of the Sun. However, the plasmas in these machines are unstable, making sustaining the process required for nuclear fusion a challenge. To ensure the plasma never touches the walls of the tokamak, which would result in heat loss (and possibly damage), a control system needs to coordinate the coils and adjust the voltage on them thousands of times per second.

Searching for a solution, DeepMind collaborated with the Swiss Plasma Center at EPFL to develop what the lab says is the first reinforcement learning system to autonomously discover how to control the coils in a tokamak. It could be used to design new kinds of tokamaks and controllers, according to DeepMind, as well as other types of “industrial and scientific” control mechanisms.

“Physics simulations are pretty good at capturing reality for the most part, since we understand the laws of physics a lot better than how people work,” Sam Geen, an astrophysicist at the University of Amsterdam, told VentureBeat via email. “It’s also a very practical use case where the results of the model failing would be quite obvious — your fusion reactor would break … Machine learning can be quite powerful for physics problems where there’s a clear link between input and output, but where what happens in-between is reasonably predictable. View more