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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.