New prediction model could improve the reliability of fusion power plants
The approach combines physics and machine learning to avoid damaging disruptions when powering down tokamak fusion machines.
Energy
Lincoln Lab unveils the most powerful AI supercomputer at any US university
Optimized for generative AI, TX-GAIN is driving innovation in biodefense, materials discovery, cybersecurity, and other areas of research and development.
Read MoreResponding to the climate impact of generative AI
Explosive growth of AI data centers is expected to increase greenhouse gas emissions. Researchers are now seeking solutions to reduce these environmental harms.
Read MoreAI 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.
Read MoreWith SCIGEN, researchers can steer AI models to create materials with exotic properties for applications like quantum computing.
Read MoreThe simulations matched results from an underground lab experiment in Switzerland, suggesting modeling could be used to validate the safety of nuclear disposal sites.
Read MoreThe MIT Energy Initiative’s annual research symposium explores artificial intelligence as both a problem and a solution for the clean energy transition.
Read MoreThe MIT-Portugal Program enters Phase 4
New phase will support continued exploration of ideas and solutions in fields ranging from AI to nanotech to climate — with emphasis on educational exchanges and entrepreneurship.
Read MoreAt the 2025 MIT Energy Conference, energy leaders from around the world discussed how to make green technologies competitive with fossil fuels.
Read MorePuzzling out climate change
Accenture Fellow Shreyaa Raghavan applies machine learning and optimization methods to explore ways to reduce transportation sector emissions.
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