Department of Energy (DoE)

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MIT researchers use AI to uncover atomic defects in materials

ai, AI (Artificial Intelligence), 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

In biology, defects are generally bad. But in materials science, defects can be intentionally tuned to give materials useful new properties. Today, atomic-scale defects are carefully introduced during the manufacturing process of products like steel, semiconductors, and solar cells to help improve strength, control electrical conductivity, optimize performance, and more.But even as defects have become […]

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Working to eliminate barriers to adopting nuclear energy

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What if there were a way to solve one of the most significant obstacles to the use of nuclear energy — the disposal of high-level nuclear waste (HLW)? Dauren Sarsenbayev, a third-year doctoral student at the MIT Department of Nuclear Science and Engineering (NSE), is addressing the challenge as part of his research.Sarsenbayev focuses on one

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

Aeronautical and astronautical engineering, Artificial Intelligence, Computer modeling, Computer science and technology, Department of Energy (DoE), DMSE, Electrical engineering and computer science (EECS), Machine Learning, Mathematics, Mechanical engineering, MIT Schwarzman College of Computing, Research, School of Engineering, School of Science, Security studies and military, Space, Supercomputing

The U.S. Department of Energy’s National Nuclear Security Administration (DOE/NNSA) recently announced that it has selected MIT to establish a new research center dedicated to advancing the predictive simulation of extreme environments, such as those encountered in hypersonic flight and atmospheric re-entry. The center will be part of the fourth phase of NNSA’s Predictive Science Academic Alliance

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New tool makes generative AI models more likely to create breakthrough materials

Artificial Intelligence, Department of Energy (DoE), Energy, Machine Learning, Materials science and engineering, National Science Foundation (NSF), Nuclear science and engineering, Quantum physics, Research, School of Engineering, Superconductivity

The artificial intelligence models that turn text into images are also useful for generating new materials. Over the last few years, generative materials models from companies like Google, Microsoft, and Meta have drawn on their training data to help researchers design tens of millions of new materials.But when it comes to designing materials with exotic

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