Machine learning reveals 5-angstrom sweet spot behind metallic glass stability
Using the second-nearest neighboring atoms to predict metallic glass stability can help researchers more accurately model the disordered solid with strong, elastic properties, according to a recent study led by University of Michigan Engineering researchers. The disordered atomic structure of metallic glasses creates a unique combination of high strength and elasticity but obscures which features control performance. Two machine learning approaches independently identified the same 5-angstrom (Å) radius as the most important to material properties. The study is published in the journal npj Computational Materials. While ductile metallic glass has been molded into components of consumer electronics, medical devices, high-end sports equipment and even spacecraft, expensive processing limits its use to these specialized applications. Understanding patterns in how the atomic structure of metallic glass impacts material properties can help researchers discover new, less costly metallic glasses for more widespread use.
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