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.