Page 2 - Mingda Li News Today

Alexander O'Brien, MIT PhD student in nuclear science and engineering, works with Professor Ju Li to explore 3D printing of ceramic-metal composites, materials that can be used in fusion power plants.

MIT researchers found evidence that topology can stabilize magnetic ordering, even well above the point at which magnetism normally breaks down. Their work reveals how topological structures known as Weyl nodes found in an exotic semi-metal can significantly increase the working temperature for magnetic devices.

Following MIT President Sally Kornbluth’s call for proposals, 27 teams of MIT scholars have been selected to develop research papers analyzing the impact of generative AI on education, companionship, music, and more.

Each year, the MIT School of Engineering honors outstanding faculty, students, and staff across its departments, labs, centers, and institutes with a number of awards. Recently, the school announced the following members of the engineering community

The MIT School of Engineering recently honored outstanding faculty, students, and staff with its 2023 awards.

MIT's School of Engineering honored junior faculty members Jacob Andreas from the Department of Electrical Engineering and Computer Science and Mingda Li from the Department of Nuclear Science and Engineering with the Junior Bose Award for outstanding education.

Researchers have found a way to streamline predicting magnetic structures and assigning magnetic classification by employing tools of machine learning, like equivariant Euclidean neural networks.

MIT researchers have found an efficient way to identify “topological” materials, whose surfaces can have different electrical or functional properties than their interiors. The approach should make it easier uncover materials that could be the basis of next-generation computer chips or quantum devices.

Using machine learning, an MIT team detected the magnetic proximity effect in topological materials that can lead to electronics without energy dissipation. The framework is readily transferable to different kinds of problems, such as the superconducting proximity effect in the field of quantum computing.

MIT PhD student Nina Andrejevic is training her machine-learning models to hunt for new and useful traits in materials. With her twin sister Jovana, she has developed a method for testing material samples to predict the presence of topological characteristics that is faster and more versatile than other methods.