Across MIT, researchers are using AI to build advanced robots, predict group decisions, and identify anomalies in unwieldy datasets. Biologists are using AI to annotate medical scans. Chemists are using it to interpret the structure and function of molecules. Cognitive scientists are using large language models, a type of AI trained on text, to understand the basis of human language. And that’s just the beginning; the integration of science and AI is a two-way process. MIT scientists are leveraging advances in computing to answer increasingly complex questions while contributing to the next generation of artificial intelligence tools.

AI model can reveal the structures of crystalline materials

MIT chemists have now come up with a new generative AI model that can make it much easier to determine the structures of these powdered crystals. The prediction model could help researchers characterize materials for use in batteries, magnets, and many other applications.

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Science and AI are not isolated subjects. We have been approaching the same goal from different perspectives, and now we are getting together.

Kaiming He

Douglas Ross (1954) Career Development Professor of Software Technology in the Department of Electrical Engineering and Computer Science

New perspectives on old questions

Jessie Micallef

Jessie Micallef is a fellow at the National Science Foundation’s AI Institute for Artificial Intelligence and Fundamental Interactions (IAIFI), a collaboration of MIT, Harvard, Northeastern, and Tufts experts applying artificial intelligence to physics research. Through IAIFI, Micallef collaborates with researchers across partner institutions and a tight-knit community of early-career fellows examining everything from astrophysics to particle interactions. Micallef studies the neutrino, an abundant subatomic particle that remains elusive to scientists.

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