MIT Energy Initiative conference spotlights research priorities amidst a changing energy landscape
Industry leaders agree collaboration is key to advancing critical technologies.
Industry leaders agree collaboration is key to advancing critical technologies.
PhD student Miranda Schwacke explores how computing inspired by the human brain can fuel energy-efficient artificial intelligence.
The new “CRESt” platform could help find solutions to real-world energy problems that have plagued the materials science and engineering community for decades.
The research center, sponsored by the DoE’s National Nuclear Security Administration, will advance the simulation of extreme environments, such as those in hypersonic flight and atmospheric reentry.
The MIT Energy Initiative’s annual research symposium explores artificial intelligence as both a problem and a solution for the clean energy transition.
With demand for cement alternatives rising, an MIT team uses machine learning to hunt for new ingredients across the scientific literature.
Rapid development and deployment of powerful generative AI models comes with environmental consequences, including increased electricity demand and water consumption.
With their recently-developed neural network architecture, MIT researchers can wring more information out of electronic structure calculations.
An electronic stacking technique could exponentially increase the number of transistors on chips, enabling more efficient AI hardware.
Progress on the energy transition depends on collective action benefiting all stakeholders, agreed participants in MITEI’s annual research conference.