Researchers from MIT and the University of Basel have developed a machine-learning framework using generative AI to analyze phase transitions in materials science. This new framework can automatically map phase diagrams and identify transitions in complex materials, surpassing traditional manual methods that are limited by human bias and theoretical assumptions. The approach uses physics-informed AI models and the Julia programming language, enabling efficient and accurate discovery of new material phases and properties, which could transform the fields of materials science and quantum physics.
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