| Abstract Scope |
This study examines CuAgZr metallic glasses (MGs) for biomedical applications, focusing on their strength, corrosion resistance, and antibacterial properties. We employed combinatorial synthesis, high-throughput characterization, and machine learning to analyze their mechanical properties. A material library was developed using direct current magnetron sputtering (DCMS), with advanced methods to assess composition, structure, and mechanical behavior. We found that high oxygen content in Cu-rich regions, due to post-deposition oxidation, significantly impacts mechanical properties. Our findings emphasize the influence 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, showcasing the potential of integrating combinatorial synthesis, high-throughput characterization, and machine learning to develop stronger, cost-effective metallic glasses. |