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Assisting Olympic athletes in enhancing their performance, step by step.

Artificial Intelligence, Community, Computer science and technology, Innovation and Entrepreneurship (I&E), Materials science and engineering, MIT.nano, Nanoscience and nanotechnology, Sports and fitness, Startups, Technology and society, UncategorizedJuly 31, 202431Views0Likes0Comments
MIT startup Striv has developed tactile sensing technology that inserts into shoes, effectively tracking force, movement, and form via algorithms that interpret tactile data. The developer, Axl Chen, initially applied his work in a virtual reality gaming context but pivoted to athletics, and several professional athletes, including US marathoner Clayton Young and Olympian Damar Forbes,…

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Materials that conduct protons could potentially lead to the development of novel eco-friendly energy solutions.

Artificial Intelligence, DMSE, Energy, Machine learning, Materials science and engineering, Research, School of Engineering, Sustainability, UncategorizedJuly 24, 202438Views0Likes0Comments
MIT engineers have identified new materials that could be more efficient conductors of protons – the nucleus of a hydrogen atom – which could pave the way for a number of climate-protecting technologies. Today's proton-conducting materials require very high temperatures, but lower-temperature alternatives could boost new technologies such as fuel cells that produce clean electricity…

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Machine learning divulges the mysteries of high-tech alloys.

Artificial Intelligence, Computer modeling, DMSE, Electrical Engineering & Computer Science (eecs), Machine learning, Materials science and engineering, Metals, Research, School of Engineering, UncategorizedJuly 19, 202433Views0Likes0Comments
Researchers from the Massachusetts Institute of Technology (MIT) are using machine learning to explore the concept of short-range order (SRO) in metallic alloys at atomic levels. The team believes that understanding SRO is key to creating high-performance alloys with unique properties but this has been a challenging area to explore. High-entropy alloys are of particular…

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Machine learning reveals the mysteries behind sophisticated alloys.

Artificial Intelligence, Computer modeling, DMSE, Electrical Engineering & Computer Science (eecs), Machine learning, Materials science and engineering, Metals, Research, School of Engineering, UncategorizedJuly 19, 202445Views0Likes0Comments
The Short-Range Order (SRO), the arrangement of atoms over small distances, plays a crucial role in materials’ properties, yet it has been understudied in metallic alloys. However, recent attention has been drawn to this concept as it is a contributing step towards developing high-performing alloys known as high-entropy alloys. Understanding how atoms self-arrange can pose…

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The AI technique dramatically accelerates the prediction of thermal characteristics of materials.

Alternative energy, Artificial Intelligence, Computer Science and Artificial Intelligence Laboratory (CSAIL), Computer science and technology, Electrical Engineering & Computer Science (eecs), Energy, Machine learning, Materials science and engineering, MIT Schwarzman College of Computing, Nuclear science and engineering, Renewable energy, Research, School of Engineering, UncategorizedJuly 17, 202434Views0Likes0Comments
An international team of researchers, including members from MIT (Massachusetts Institute of Technology), has developed a machine learning-based approach to predict the thermal properties of materials. This understanding could help improve energy efficiency in power generation systems and microelectronics. The research focuses on phonons - subatomic particles that carry heat. Properties of these particles affect…

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A novel approach to computer vision accelerates the screening process of electronic components.

3-D printing, Algorithms, Artificial Intelligence, Automation, Computer vision, Imaging, Machine learning, Materials science and engineering, Mechanical engineering, Renewable energy, Research, School of Engineering, Solar, Sustainability, UncategorizedJune 12, 202432Views0Likes0Comments
Solar cells, transistors, LEDs, and batteries with boosted performance require better electronic materials which are often discovered from novel compositions. Scientists have turned to AI tools to identify potential materials from millions of chemical formulations, with engineers developing machines that can print hundreds of samples at a time, based on compositions identified by AI algorithms.…

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Engineers at MIT have devised a method to ascertain the behaviour of material surfaces.

Artificial Intelligence, Chemistry, Department of Defense (DoD), DMSE, Machine learning, Materials science and engineering, National Science Foundation (NSF), Research, School of Engineering, UncategorizedMarch 27, 202435Views0Likes0Comments
A group of MIT researchers has developed a machine learning (ML) approach that could revolutionize the way we design catalysts for chemical reactions. The method simplifies the intricate process of designing new compounds or alloys, traditionally dependent on the intuition of experienced chemists, by using ML to provide more detailed information than conventional techniques can. The…

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Engineers at MIT have devised a method to ascertain the behavior of material surfaces.

Artificial Intelligence, Chemistry, Department of Defense (DoD), DMSE, Machine learning, Materials science and engineering, National Science Foundation (NSF), Research, School of Engineering, UncategorizedMarch 26, 202435Views0Likes0Comments
Researchers from MIT have developed a machine learning approach that could replace the intuition-based methods typically used in the creation of catalysts. The team, led by graduate student Xiaochen Du, devised a system that offers more detailed insights than conventional techniques, identifying previously undiscovered atomic configurations in a material that had been researched for three…

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Engineers from MIT have devised a method to decipher the behavior of material surfaces.

Artificial Intelligence, Chemistry, Department of Defense (DoD), DMSE, Machine learning, Materials science and engineering, National Science Foundation (NSF), Research, School of Engineering, UncategorizedMarch 7, 202439Views0Likes0Comments
MIT researchers have developed a machine learning-based method for designing new compounds or alloys for use as catalysts in chemical reactions. Traditional methods of designing such materials rely on static observations of a single configuration, out of millions of possibilities, and the intuition of experienced chemists. However, the new method employs machine learning algorithms to…

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Employing artificial intelligence to unveil rigid and strong microstructures.

3-D printing, Algorithms, Artificial Intelligence, Computer Science and Artificial Intelligence Laboratory (CSAIL), Computer science and technology, Design, Electrical Engineering & Computer Science (eecs), Materials science and engineering, MIT Schwarzman College of Computing, Research, School of Engineering, UncategorizedMarch 7, 202437Views0Likes0Comments
A team of researchers at the MIT Computer Science and Artificial Intelligence Laboratory (CSAIL) has developed a revolutionary computational design system that enhances the performance of microstructured materials. These materials, ubiquitous in everything from cars to airplanes, offer essential durability and strength. This project, led by Beichen Li, a CSAIL affiliate and an MIT PhD…

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