Generative machine learning models have shown notable success in identifying architectures for metamaterials - materials whose behavior is determined primarily by their internal organization - that match specific target properties. By examining kirigami metamaterials, in which dependencies between cuts yield complex design restrictions, we demonstrate that this perceived success in the employment of generative models for metamaterials might be akin to survivorship bias. We assess the performance of the four most popular generative models - the Variational Autoencoder (VAE), the Generative Adversarial Network (GAN), the Wasserstein GAN (WGAN), and the Denoising Diffusion Probabilistic Model (DDPM) - in generating kirigami structures. Prohibiting cut intersections can prevent the identification of an appropriate similarity measure for kirigami metamaterials, significantly impacting the effectiveness of VAE and WGAN, which rely on the Euclidean distance - a metric shown to be unsuitable for considered geometries. This imposes significant limitations on employing modern generative models for the creation of diverse metamaterials.

翻译：生成式机器学习模型在识别具有特定目标属性的超材料（其行为主要由内部组织决定）的架构方面取得了显著成功。通过研究剪纸超材料（其中切割间的依赖关系导致了复杂的设计限制），我们证明这种在超材料生成模型中看似成功的应用可能类似于幸存者偏差。我们评估了四种最流行的生成模型——变分自编码器（VAE）、生成对抗网络（GAN）、Wasserstein GAN（WGAN）和去噪扩散概率模型（DDPM）——在生成剪纸结构中的表现。禁止切割交叉会阻碍为剪纸超材料找到合适的相似性度量，从而显著影响依赖欧几里得距离的VAE和WGAN的有效性——这种度量被证明不适用于所考虑的几何形状。这为使用现代生成模型创建多样化超材料带来了显著限制。