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Artificial Intelligence

Cracking the Glass Code: Machine Learning Reveals the Hidden Architecture of Metallic Stability

Researchers have utilized dual machine learning frameworks to identify a specific 5-angstrom atomic scale—primarily involving second-nearest neighbors—that dictates the structural stability of metallic glasses. This breakthrough offers a precision roadmap for designing new, cost-effective amorphous materials with the strength of steel and the elasticity of polymers.

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