MIT Professor Aims to Advance Nuclear Energy Through AI

Dean Price is working to design safer, more economical nuclear reactors using machine learning techniques.

Apr. 4, 2026 at 8:06am

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A highly structured abstract painting in soft, flat colors featuring sweeping geometric arcs, concentric circles, and precise botanical spirals, conveying the complex physical forces at work within a nuclear reactor core.An abstract illustration captures the intricate interplay of physical processes within a next-generation nuclear reactor, harnessing the power of artificial intelligence to unlock new design possibilities.Urbana Today

Dean Price, an MIT assistant professor in the Department of Nuclear Science and Engineering, is on a mission to help design and usher in a new class of nuclear reactors that build on the safety, economics, and reliability of the existing nuclear fleet. He is exploring the use of artificial intelligence and machine learning to improve the design and operation of advanced nuclear reactors, including small modular reactors and microreactors.

Why it matters

Nuclear energy currently provides nearly 20% of the United States' electricity, but Price believes the country needs to do much more to harness nuclear power as an alternative to fossil fuels. By advancing nuclear technology through AI, Price aims to make nuclear energy a more viable and attractive option for carbon-free power generation.

The details

Price's research focuses on multiphysics modeling, which looks at how various physical processes in a nuclear reactor core, such as neutronics and thermal hydraulics, interact. He is exploring ways to use AI and machine learning to bypass the computationally intensive process of solving complex nonlinear equations required for these simulations. This could help improve the design of advanced reactors and enable more intelligent control decisions to operate them safely and economically.

  • Price joined the MIT faculty in September 2025.
  • He co-taught a nuclear design course at MIT in the fall of 2025.

The players

Dean Price

An MIT assistant professor in the Department of Nuclear Science and Engineering and the Atlantic Richfield Career Development Professor in Energy Studies. He is working to advance nuclear energy technology through the use of artificial intelligence and machine learning.

Curtis Smith

The KEPCO Professor of the Practice of Nuclear Science and Engineering at MIT, who co-taught a design course with Price.

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What they’re saying

“Nuclear energy has been a tremendous part of our nation's energy infrastructure for the past 60 years, and the number of people who maintain that infrastructure is incredibly small. By becoming a nuclear engineer, you become one of a select number of people responsible for carbon-free energy generation in the United States.”

— Dean Price, MIT Assistant Professor

“If you ever want to change your power level, or do anything with the reactor, the temperature of the fuel is a critical input that you need to know. Multiphysics modeling allows us to correlate the fission neutronics processes with a thermal property, temperature. That, in turn, can help us predict how the reactor will behave under different conditions.”

— Dean Price, MIT Assistant Professor

“By really pinning down those relationships, we can make better design decisions in the early stages. And when that technology is developed and deployed, AI can help us make more intelligent control decisions that will enable us to operate our reactors in a safer and more economical way.”

— Dean Price, MIT Assistant Professor

What’s next

Price plans to continue his research into using AI and machine learning to improve the design and operation of advanced nuclear reactors, with the goal of making nuclear energy a more viable and attractive option for carbon-free power generation.

The takeaway

By leveraging the power of artificial intelligence and machine learning, MIT Professor Dean Price is working to overcome some of the key challenges facing the nuclear industry and help usher in a new era of safer, more economical nuclear power generation.