We present a study directly measuring the electron‐beam‐induced plasticity of amorphous Al2O3 coatings. Core–shell nanostructures are employed as small‐scale model systems for two‐dimensional coatings made by atomic layer deposition (ALD). Copper nanowires (NWs) are used as substrates for ALD deposition, representing a model system for interconnects commonly found in integrated circuits. Experiments are performed in situ in a transmission electron microscope (TEM) and further analyzed with electron energy loss spectroscopy (EELS). Our in situ TEM tensile experiments reveal the highly plastic behavior of the ALD shell, which withstands a maximum strain of 188%. Comparable samples under beam‐off conditions show a brittle fracture, which underlines the effect of electron irradiation. The electron‐beam‐activated bond switching within the amorphous network enables compensation of the applied tensile strain, leading to viscous flow. By incorporating an intermediate nanocrystalline layer within the Al2O3 shell, the plasticity is suppressed and brittle fracture occurs. This work directly demonstrates the tuning of mechanical properties in amorphous ALD structures through electron irradiation.

In this work, the CuAgZr metallic glasses (MGs) are investigated, a promising material for biomedical applications due to their high strength, corrosion resistance, and antibacterial activity. Using an integrated approach of combinatorial synthesis, high-throughput characterization, and machine learning (ML), the mechanical properties of CuAgZr MGs are efficiently explored. The investigation find that post-deposition oxidation in inter-columnar regions with looser packing causes high oxygen content in Cu-rich regions, significantly affecting the alloys mechanical behavior. The study also reveals that nanoscale structural features greatly impact plastic yielding and flow in the alloys. ML algorithms are tested, and the multi-layer perceptron algorithm produced satisfactory predictions for the alloys hardness of untested alloys, providing valuable clues for future research. The work demonstrates the potential of using combinatorial synthesis, high-throughput characterization, and ML  techniques to facilitate the development of new MGs with improved strength and economic feasibility.