Confronting the AI/energy conundrum
The MIT Energy Initiative’s annual research symposium explores artificial intelligence as both a problem and a solution for the clean energy transition.
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AI stirs up the recipe for concrete in MIT study
With demand for cement alternatives rising, an MIT team uses machine learning to hunt for new ingredients across the scientific literature.
Explained: Generative AI’s environmental impact
Rapid development and deployment of powerful generative AI models comes with environmental consequences, including increased electricity demand and water consumption.
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.
MIT engineers grow “high-rise” 3D chips
An electronic stacking technique could exponentially increase the number of transistors on chips, enabling more efficient AI hardware.
Ensuring a durable transition
Progress on the energy transition depends on collective action benefiting all stakeholders, agreed participants in MITEI’s annual research conference.
Nanoscale transistors could enable more efficient electronics
Researchers are leveraging quantum mechanical properties to overcome the limits of silicon semiconductor technology.
MIT Schwarzman College of Computing launches postdoctoral program to advance AI across disciplines
The new Tayebati Postdoctoral Fellowship Program will support leading postdocs to bring cutting-edge AI to bear on research in scientific discovery or music.
Proton-conducting materials could enable new green energy technologies
Analysis and materials identified by MIT engineers could lead to more energy-efficient fuel cells, electrolyzers, batteries, or computing devices.