用生成模型反推合金配方，高效设计高性能形状记忆材料。
Generative Inversion for Property-Targeted Materials Design: Application to Shape Memory Alloys
- 通过预训练GAN结合属性预测模型，在潜空间优化生成目标成分。
- 新合金实现404℃转变温度、9.9 J/cm³做功能力，优于现有镍钛合金。
- 适合材料设计与人工智能结合的研究者参考。
高转变温度和大机械功输出的形状记忆合金（SMAs）设计仍是功能材料工程中的长期挑战。本文提出一种基于生成对抗网络（GAN）反演的数据驱动框架，用于高性能SMAs的逆向设计。通过将预训练GAN与属性预测模型结合，采用梯度优化潜空间，直接生成满足用户指定性能目标的合金成分与工艺参数。该框架经五种镍钛基SMAs的合成与表征实验验证。其中Ni₄₉.₈Ti₂₆.₄Hf₁₈.₆Zr₅.₂合金实现404℃的高转变温度、9.9 J/cm³的大机械功输出、43 J/g的相变焓及29℃的热滞回，优于现有镍钛合金。性能提升归因于显著的相变体积变化及由锆、铪缓慢扩散所形成的细小弥散分布的Ti₂Ni型析出物，以及具有局部应变场的半共格界面。本研究证明，GAN反演为复杂合金的性能靶向发现提供了一条高效且可推广的路径。
原文摘要 · Abstract (English)
The design of shape memory alloys (SMAs) with high transformation temperatures and large mechanical work output remains a longstanding challenge in functional materials engineering. Here, we introduce a data-driven framework based on generative adversarial network (GAN) inversion for the inverse design of high-performance SMAs. By coupling a pretrained GAN with a property prediction model, we perform gradient-based latent space optimization to directly generate candidate alloy compositions and processing parameters that satisfy user-defined property targets. The framework is experimentally validated through the synthesis and characterization of five NiTi-based SMAs. Among them, the Ni$_{49.8}$Ti$_{26.4}$Hf$_{18.6}$Zr$_{5.2}$ alloy achieves a high transformation temperature of 404 $^\circ$C, a large mechanical work output of 9.9 J/cm$^3$, a transformation enthalpy of 43 J/g , and a thermal hysteresis of 29 °C, outperforming existing NiTi alloys. The enhanced performance is attributed to a pronounced transformation volume change and a finely dispersed of Ti$_2$Ni-type precipitates, enabled by sluggish Zr and Hf diffusion, and semi-coherent interfaces with localized strain fields. This study demonstrates that GAN inversion offers an efficient and generalizable route for the property-targeted discovery of complex alloys.
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