| Abstract Scope |
This study explores CuAgZr metallic glasses (MGs) for biomedical applications, emphasizing their strength, corrosion resistance, and antibacterial properties. Utilizing combinatorial synthesis, high-throughput characterization, and machine learning, we investigate their mechanical properties. A material library was created using direct current magnetron sputtering (DCMS), with advanced techniques assessing composition, structure, and mechanical behavior. We discovered that high oxygen content in Cu-rich regions, due to post-deposition oxidation, significantly affects mechani-cal properties. Our findings highlight the impact of nanoscale structures on plastic yielding and flow, correlating atomic size mismatch, oxygen content, and hardness. The multi-layer perceptron (MLP) algorithm effectively predicts hardness in untested alloys, demonstrating the potential of combining combinatorial synthesis, high-throughput characterization, and machine learning for developing stronger, economically feasible metallic glasses. |