MIT and University of Basel researchers have developed a generative AI framework to map phase diagrams in novel physical systems. This machine-learning approach, more efficient than traditional manual methods, uses generative models to identify phase transitions without extensive labeled data. This method could help investigate thermodynamic properties and detect quantum entanglement. It automates the discovery of unknown phases of matter, enhancing computational efficiency in physics research.
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