Nuclear science and engineering
Nuclear science and engineering

Nuclear science and engineering

Artificial Intelligence Computer modeling Energy Faculty machine learning Nuclear power and reactors Nuclear science and engineering Profile Renewable energy School of Engineering Sustainability

Working to advance the nuclear renaissance

Dean Price, assistant professor in the Department of Nuclear Science and Engineering, sees a bright future for nuclear power, and believes AI can help us realize that vision.

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MIT researchers use AI to uncover atomic defects in materials

A new model measures defects that can be leveraged to improve materials’ mechanical strength, heat transfer, and energy-conversion efficiency.

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Working to eliminate barriers to adopting nuclear energy

Nuclear waste continues to be a bottleneck in the widespread use of nuclear energy, so doctoral student Dauren Sarsenbayev is developing models to address the problem.

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The brain power behind sustainable AI

PhD student Miranda Schwacke explores how computing inspired by the human brain can fuel energy-efficient artificial intelligence.

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AI system learns from many types of scientific information and runs experiments to discover new materials

The new “CRESt” platform could help find solutions to real-world energy problems that have plagued the materials science and engineering community for decades.

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New tool makes generative AI models more likely to create breakthrough materials

With SCIGEN, researchers can steer AI models to create materials with exotic properties for applications like quantum computing.

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AI and machine learning for engineering design

Popular mechanical engineering course applies machine learning and AI theory to real-world engineering design.

Artificial Intelligence Computer modeling Energy machine learning Nuclear power and reactors Nuclear science and engineering Nuclear security and policy Research School of Engineering

Model predicts long-term effects of nuclear waste on underground disposal systems

The simulations matched results from an underground lab experiment in Switzerland, suggesting modeling could be used to validate the safety of nuclear disposal sites.

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MIT Maritime Consortium sets sail

A new international collaboration unites MIT and maritime industry leaders to develop nuclear propulsion technologies, alternative fuels, data-powered strategies for operation, and more.

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New computational chemistry techniques accelerate the prediction of molecules and materials

With their recently-developed neural network architecture, MIT researchers can wring more information out of electronic structure calculations.