Team creates map for production of eco-friendly metals
New understanding of metal electrolysis could help optimize production of metals like lithium and iron.
New understanding of metal electrolysis could help optimize production of metals like lithium and iron.
Study finds activating a Clean Air Act provision could deliver major climate, health, and economic benefits.
The student pitch competition included a variety of solutions addressing water access, usage, and maintenance.
Workshop hosted by MIT’s Climate and Sustainability Consortium, MIT-IBM Watson AI Lab, and the MIT Schwarzman College of Computing highlights how new approaches to computing can save energy and help the planet.
Linking techniques from machine learning with advanced numerical simulations, MIT researchers take an important step in state-of-the-art predictions for fusion plasmas.
Students are driving innovative research to promote water and food security for all.
New study reveals multiple pathways for a successful energy transition by 2050.
Special Presidential Envoy for Climate John Kerry calls the initiative “classic MIT.”
Over the past four years, the mechanical engineering community at MIT has utilized their diverse skills and passions to develop solutions for the health of the planet.
Through the year-long MCSC Climate and Sustainability Scholars Program, students have the opportunity to lead research projects.
A new platform will unite climate models, impact predictions, random control trial evaluations, and humanitarian services to bring cutting-edge tools to Bangladeshi communities.
A Climate Grand Challenges flagship project aims to reduce agriculture-driven emissions while making food crop plants heartier and more nutritious.
To better inform local policy in the face of changing weather extremes, MIT researchers seek to advance the modeling of long-term weather risks.
The Center for Electrification and Decarbonization of Industry unites MIT climate researchers to create scalable clean energy solutions under one roof.
Perovskite materials would be superior to silicon in PV cells, but manufacturing such cells at scale is a huge hurdle. Machine learning can help.