Machine learning modeling for the prediction of plastic properties in metallic glasses.

ABSTRACT: Metallic glasses are one of the most interesting mechanical materials studied in the last years, but as amorphous solids, they differ strongly from their crystalline counterparts. This matter can be addressed with the development and application of predictive techniques capable to describe the plastic regime. Here, machine learning models were employed for the prediction of plastic properties in CuZr metallic glasses. To this aim, 100 different samples were subjected to tensile tests by means of molecular dynamics simulations. A total of 17 materials properties were calculated and explored using statistical analysis. Strong correlations were found for stoichiometry, temperature, structural, and elastic properties with plastic properties. Three regression models were employed for the predic

SUBMITTER: Amigo N 

PROVIDER: S-EPMC9825623 | biostudies-literature | 2023 Jan

REPOSITORIES: biostudies-literature