DMSE
DMSE

DMSE

Alternative energy Artificial Intelligence Chemical engineering DMSE Electrical engineering and computer science (EECS) Emissions Energy Energy efficiency Faculty Laboratory for Information and Decision Systems (LIDS) Lincoln Laboratory MIT Energy Initiative Renewable energy School of Engineering Special events and guest speakers Staff Sustainability

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.

Artificial Intelligence Civil and environmental engineering Cleaner industry Concrete Concrete Sustainability Hub DMSE Emissions Environment Infrastructure Materials science and engineering Research School of Engineering Sustainability Technology and society

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.

Algorithms Artificial Intelligence Cleaner industry Climate Computer Science and Artificial Intelligence Laboratory (CSAIL) Computer science and technology Data DMSE Electrical engineering and computer science (EECS) Emissions Energy Environment Human-computer interaction machine learning MIT Schwarzman College of Computing School of Engineering Sustainability Sustainable computing Water

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.

Artificial Intelligence Computer science and technology DMSE machine learning Materials science and engineering Networks Nuclear science and engineering Quantum mechanics Research School of Engineering Semiconductors

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.

2-D Artificial Intelligence Computer chips DMSE Electronics Materials science and engineering Mechanical engineering Research School of Engineering Semiconductors

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.

Artificial Intelligence Chemical engineering Cleaner industry Climate change Department of Energy (DoE) DMSE Energy Energy storage Faculty Fusion Government MIT Energy Initiative Nuclear power and reactors Policy Renewable energy School of Architecture and Planning School of Engineering Special events and guest speakers Startups Students Sustainability Urban studies and planning

Ensuring a durable transition

Progress on the energy transition depends on collective action benefiting all stakeholders, agreed participants in MITEI’s annual research conference.

Artificial Intelligence Computer science and technology DMSE Electrical engineering and computer science (EECS) Electronics Energy machine learning Materials science and engineering Microsystems Technology Laboratories MIT Schwarzman College of Computing MIT.nano Nanoscience and nanotechnology Nuclear science and engineering Research School of Engineering

Nanoscale transistors could enable more efficient electronics

Researchers are leveraging quantum mechanical properties to overcome the limits of silicon semiconductor technology.

Artificial Intelligence Awards, honors and fellowships Biology Brain and cognitive sciences Chemical engineering chemistry Computer Science and Artificial Intelligence Laboratory (CSAIL) DMSE Funding Graduate, postdoctoral MIT Schwarzman College of Computing Music Physics School of Engineering School of Humanities Arts and Social Sciences School of Science

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.

Artificial Intelligence DMSE Energy machine learning Materials science and engineering Research School of Engineering Sustainability

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.

Artificial Intelligence Computer modeling DMSE Electrical Engineering & Computer Science (eecs) machine learning Materials science and engineering Metals Research School of Engineering

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.