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.

Originally published at Phys.org