Tackling Climate Change with Machine Learning Priya L. Donti Assistant Professor, MIT Department of Electrical Engineering and Computer Science Assistant Professor, MIT Laboratory for Information & Decision Systems Co-founder and Chair, Climate Change AI
Climate change is one of the greatest challenges that society faces today, requiring rapid action from across society. In this talk, I will describe how machine learning can be a potentially powerful tool for addressing climate change when applied in coordination with policy, engineering, and other areas of action. From energy to agriculture to disaster response, I will describe high-impact problems where machine learning can help through avenues such as distilling decision-relevant information, optimizing complex systems, and accelerating scientific experimentation. I will also describe key considerations for the responsible development and deployment of such work. While this talk will primarily discuss opportunities for machine learning to help address climate change, it is worth noting that machine learning is a general-purpose technology that can be used for applications that both help and hinder climate action. In addition, machine learning has its own computational and hardware footprint. I will therefore briefly present a framework for understanding and contextualizing machine learning’s overall climate impacts, and describe associated considerations for machine learning research and practice as a whole.
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Principal Investigator Yuriy Román
Decarbonizing transportation, the grid, and heavy industries depends on the success of both short- and long-duration energy storage solutions. Through novel material design and chemistry, my lab addresses critical challenges in developing affordable, sustainable, and reliable energy storage technologies. For short (to medium)-duration storage, we design and develop new cathode materials for sodium-ion batteries rich in manganese and iron. Our goal is to achieve energy densities comparable to lithium-ion batteries but at lower costs, without relying on critical minerals, thereby accelerating the transition to more sustainable energy storage. For long-duration storage, we have developed groundbreaking pathways for producing hydrogen (H₂) and ammonia (NH₃) using subsurface chemistry. By harnessing redox reactions on Fe-rich rocks and utilizing the Earth's natural heat and pressure, we demonstrate the potential for stimulated geological H₂ and NH₃ production. These methods achieve near-zero CO₂ emissions while remaining cost-competitive with existing technologies. Our work integrates advanced materials design with sustainable chemistry to provide scalable, impactful solutions for a decarbonized future.
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