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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.
In a September 2020 essay in Nature Energy, three scientists posed several "grand challenges" - one of which was to find suitable materials for thermal.