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New physics-based machine-learning method speeds search for 2D quantum materials
Researchers at The University of Manchester have developed a new computational approach to help identify two-dimensional materials that may host unusual quantum behavior.
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Researchers at The University of Manchester have created a new computational method to identify two-dimensional materials with potential for unusual quantum behavior. This approach focuses on materials with "flat bands," electronic states characterized by low electron kinetic energy. The method leverages physics-based machine learning to analyze these materials and predict their properties. The development of this computational tool may aid in the discovery of new 2D quantum materials, which could have significant implications for various fields, including materials science and condensed matter physics.
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