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.
DMSE
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.
Machine learning unlocks secrets to advanced alloys
An MIT team uses computer models to measure atomic patterns in metals, essential for designing custom materials for use in aerospace, biomedicine, electronics, and more.
School of Engineering welcomes new faculty
Fifteen new faculty members join six of the school’s academic departments.
From steel engineering to ovarian tumor research
Ashutosh Kumar, a materials science and engineering PhD student and MathWorks Fellow, applies his eclectic skills to studying the relationship between bacteria and cancer.