Constructing material network representations for intelligent amorphous alloy design

Designing high-performance amorphous alloys for various applications is demanding, as the process heavily depends on empirical laws and unlimited attempts. The high-cost and low-efficiency nature of traditional strategies prevents effective sampling in the enormous material space. Here, we propose material networks to accelerate the discovery of binary and ternary amorphous alloys. The network topologies reveal hidden material candidates that were obscured by traditional tabular data representations. By scrutinizing the amorphous alloys synthesized in different years, we construct dynamical material networks to track the history of alloy discovery. We find that some innovative materials designed in the past were encoded in the networks, demonstrating their predictive power in guiding new alloy design. These material networks show physical similarities with several real-world networks in our everyday lives. Our findings pave a new path toward intelligent material design, especially for complex alloys.