Researchers used generative AI to develop a physics-informed technique to classify phase transitions in materials or physical systems that is much more efficient than existing machine-learning approaches. The work was led by researchers at MIT and the University of Basel.

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Scientists have developed a machine-learning framework using generative AI models to map out phase diagrams for novel physical systems. Their approach is more efficient than manual techniques and does not require large labeled training datasets. By recognizing phase changes and detecting transitions, this technique could aid in the investigation of thermodynamic properties, detection of entanglement in quantum systems, and autonomous discovery of unknown phases of matter.

Scientists use generative AI to answer complex questions in physics