Materials science and engineering

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3 Questions: On the future of AI and the mathematical and physical sciences

ai, AI (Artificial Intelligence), Algorithms, Artificial Intelligence, Astronomy and astrophysics, Chemistry, Computer science and technology, data, Faculty, IDSS, Interview, Laboratory for Nuclear Science, Machine Learning, Materials science and engineering, Mathematics, MIT Schwarzman College of Computing, National Science Foundation (NSF), Physics, Research, School of Science

Curiosity-driven research has long sparked technological transformations. A century ago, curiosity about atoms led to quantum mechanics, and eventually the transistor at the heart of modern computing. Conversely, the steam engine was a practical breakthrough, but it took fundamental research in thermodynamics to fully harness its power. Today, artificial intelligence and science find themselves at a […]

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Accelerating science with AI and simulations

ai, AI (Artificial Intelligence), Artificial Intelligence, DMSE, Faculty, Machine Learning, Materials science and engineering, Profile, Research, School of Engineering

For more than a decade, MIT Associate Professor Rafael Gómez-Bombarelli has used artificial intelligence to create new materials. As the technology has expanded, so have his ambitions.Now, the newly tenured professor in materials science and engineering believes AI is poised to transform science in ways never before possible. His work at MIT and beyond is

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How generative AI can help scientists synthesize complex materials

ai, AI (Artificial Intelligence), Artificial Intelligence, Chemical engineering, Computer science and technology, DMSE, Machine Learning, Materials science and engineering, National Science Foundation (NSF), Research, School of Engineering

Generative artificial intelligence models have been used to create enormous libraries of theoretical materials that could help solve all kinds of problems. Now, scientists just have to figure out how to make them.In many cases, materials synthesis is not as simple as following a recipe in the kitchen. Factors like the temperature and length of

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New materials could boost the energy efficiency of microelectronics

Artificial Intelligence, Computer chips, Computer science and technology, Electrical engineering and computer science (EECS), Electronics, Energy, Materials science and engineering, Microsystems Technology Laboratories, MIT Schwarzman College of Computing, MIT.nano, Nanoscience and nanotechnology, Research, School of Engineering, Sustainable computing

MIT researchers have developed a new fabrication method that could enable the production of more energy efficient electronics by stacking multiple functional components on top of one existing circuit.In traditional circuits, logic devices that perform computation, like transistors, and memory devices that store data are built as separate components, forcing data to travel back and

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Printable aluminum alloy sets strength records, may enable lighter aircraft parts

3-D printing, Artificial Intelligence, DMSE, Machine Learning, Materials science and engineering, Mechanical engineering, MIT.nano, Research, School of Engineering

MIT engineers have developed a printable aluminum alloy that can withstand high temperatures and is five times stronger than traditionally manufactured aluminum.The new printable metal is made from a mix of aluminum and other elements that the team identified using a combination of simulations and machine learning, which significantly pruned the number of possible combinations

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Checking the quality of materials just got easier with a new AI tool

Artificial Intelligence, Imaging, Light, Machine Learning, manufacturing, Materials science and engineering, Mechanical engineering, Research, School of Engineering

Manufacturing better batteries, faster electronics, and more effective pharmaceuticals depends on the discovery of new materials and the verification of their quality. Artificial intelligence is helping with the former, with tools that comb through catalogs of materials to quickly tag promising candidates.But once a material is made, verifying its quality still involves scanning it with

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

Artificial Intelligence, DMSE, Energy, Machine Learning, Materials science and engineering, MIT.nano, Nanoscience and nanotechnology, Nuclear science and engineering, Research, School of Engineering

Machine-learning models can speed up the discovery of new materials by making predictions and suggesting experiments. But most models today only consider a few specific types of data or variables. Compare that with human scientists, who work in a collaborative environment and consider experimental results, the broader scientific literature, imaging and structural analysis, personal experience

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