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Scientists have developed a new machine-learning framework that can automatically map out phase diagrams for novel physical systems, making it easier to understand and detect phase transitions in complex systems. This approach is more efficient and does not require large, labeled training datasets, potentially allowing for autonomous discovery of new properties of matter.
