The brain power behind sustainable AI
PhD student Miranda Schwacke explores how computing inspired by the human brain can fuel energy-efficient artificial intelligence.
Nuclear science and engineering
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.
Read MoreWith SCIGEN, researchers can steer AI models to create materials with exotic properties for applications like quantum computing.
Read MoreAI and machine learning for engineering design
Popular mechanical engineering course applies machine learning and AI theory to real-world engineering design.
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 MoreA new international collaboration unites MIT and maritime industry leaders to develop nuclear propulsion technologies, alternative fuels, data-powered strategies for operation, and more.
Read MoreNew 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.
Read MoreUnlocking the hidden power of boiling — for energy, space, and beyond
Associate Professor Matteo Bucci’s research sheds new light on an ancient process, to improve the efficiency of heat transfer in many industrial systems.
Read MoreNanoscale transistors could enable more efficient electronics
Researchers are leveraging quantum mechanical properties to overcome the limits of silicon semiconductor technology.
Read MoreAI method radically speeds predictions of materials’ thermal properties
The approach could help engineers design more efficient energy-conversion systems and faster microelectronic devices, reducing waste heat.
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