用材料网络图谱加速非晶合金设计，挖掘隐藏候选材料
Constructing material network representations for intelligent amorphous alloys design
- 构建动态材料网络，揭示传统表格无法展现的材料关联
- 网络能识别历史创新材料，具备指导新合金设计的预测能力
- 方法适用于复杂合金设计，对材料研发人员极具参考价值
高性能非晶合金的设计在众多应用中至关重要，但传统方法高度依赖经验法则和大量试错，存在成本高、效率低的问题，难以在庞大的材料空间中有效采样。本文提出材料网络模型，用于加速二元与三元非晶合金的发现。通过分析不同年份合成的非晶合金，构建动态材料网络以追踪合金发现的历史进程。研究发现，部分过去创新材料已被编码于网络之中，证明其具有预测新合金设计的能力。材料网络在结构上表现出与现实世界网络（如社交网络）相似的物理特性。该成果为智能材料设计，特别是复杂合金的设计开辟了新路径。
原文摘要 · Abstract (English)
Designing high-performance amorphous alloys is demanding for various applications. But this process intensively relies on empirical laws and unlimited attempts. The high-cost and low-efficiency nature of the 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 the 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 daily lives. Our findings pave a new way for intelligent materials design, especially for complex alloys.
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