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.
With demand for cement alternatives rising, an MIT team uses machine learning to hunt for new ingredients across the scientific literature.
At the 2025 MIT Energy Conference, energy leaders from around the world discussed how to make green technologies competitive with fossil fuels.
Agreement between MIT Microsystems Technology Laboratories and GlobalFoundries aims to deliver power efficiencies for data centers and ultra-low power consumption for intelligent devices at the edge.
Accenture Fellow Shreyaa Raghavan applies machine learning and optimization methods to explore ways to reduce transportation sector emissions.
Station A, founded by MIT alumni, makes the process of buying clean energy simple for property owners.
Providing electricity to power-hungry data centers is stressing grids, raising prices for consumers, and slowing the transition to clean energy.
Rapid development and deployment of powerful generative AI models comes with environmental consequences, including increased electricity demand and water consumption.
Progress on the energy transition depends on collective action benefiting all stakeholders, agreed participants in MITEI’s annual research conference.
The challenge asked teams to develop AI algorithms to track and predict satellites’ patterns of life in orbit using passively collected data
Together, the Hasso Plattner Institute and MIT are working toward novel solutions to the world’s problems as part of the Designing for Sustainability research program.