Machine-learning assisted local stress evaluation and atomistic modelling to inquire Ti-enrichment in W-Ti nanocrystalline films

Abstract Nanocrystalline metallic alloy thin films provide a variety of interesting properties. A disadvantage concerns their inherent instability and generation of residual stresses. Here, we present a unique framework incorporating machine learning assisted experimental and modelling methods to perceive the impact of the minority element concentration on the generated residual stresses within a nanocrystalline thin film. As a candidate system we use a W-Ti alloy thin film with different Ti concentrations. We perform machine-learning assisted local residual stress measurements, correlate the results with accompanied microstructure and elemental analysis and apply density functional theory. We inquire why the experimental observed Ti enrichment can be strongly reduced at smaller concentrations and discuss how it significantly effects the stress stored in the nanocrystalline thin film. The presented perception is highly crucial to yield future design guidelines for more stable nanocrystalline thin films.