DMSE
DMSE

DMSE

Artificial Intelligence Batteries Computer modeling Department of Energy (DoE) DMSE Electrical engineering and computer science (EECS) machine learning Manufacturing Materials science and engineering Nanoscience and nanotechnology National Science Foundation (NSF) Nuclear science and engineering Research School of Engineering Semiconductors Solar

MIT researchers use AI to uncover atomic defects in materials

A new model measures defects that can be leveraged to improve materials’ mechanical strength, heat transfer, and energy-conversion efficiency.

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Accelerating science with AI and simulations

Associate Professor Rafael Gómez-Bombarelli has spent his career applying AI to improve scientific discovery. Now he believes we are at an inflection point.

Artificial Intelligence Chemical engineering Computer science and technology DMSE machine learning Materials science and engineering National Science Foundation (NSF) Research School of Engineering

How generative AI can help scientists synthesize complex materials

MIT researchers’ DiffSyn model offers recipes for synthesizing new materials, enabling faster experimentation and a shorter journey from hypothesis to use.

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MIT Energy Initiative conference spotlights research priorities amidst a changing energy landscape

Industry leaders agree collaboration is key to advancing critical technologies.

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The brain power behind sustainable AI

PhD student Miranda Schwacke explores how computing inspired by the human brain can fuel energy-efficient artificial intelligence.

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AI system learns from many types of scientific information and runs experiments to discover new materials

The new “CRESt” platform could help find solutions to real-world energy problems that have plagued the materials science and engineering community for decades.

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DoE selects MIT to establish a Center for the Exascale Simulation of Coupled High-Enthalpy Fluid–Solid Interactions

The research center, sponsored by the DoE’s National Nuclear Security Administration, will advance the simulation of extreme environments, such as those in hypersonic flight and atmospheric reentry.

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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.

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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.

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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.