深度学习用于大型X射线衍射数据集的可视化和新颖性检测
Deep learning for visualization and novelty detection in large X-ray diffraction datasets
摘要：
我们应用变分自动编码器（VAE）的X射线衍射（XRD）数据分析的模拟和实验的薄膜数据。我们发现，晶体结构表示学习的VAE揭示潜在的信息，如纹理衍射图案的结构相似性。虽然其他人工智能（AI）代理可以有效地将XRD数据分类为已知的相，但类似的条件VAE在知道它不知道的东西，快速识别新的相和混合物方面非常有效。这些功能表明，VAE是一种有价值的人工智能代理，用于材料发现和实时和事后分析期间的XRD测量。
英文摘要：
We apply variational autoencoders (VAE) to X-ray diffraction (XRD) data analysis on both simulated and experimental thin-film data. We show that crystal structure representations learned by a VAE reveal latent information, such as the structural similarity of textured diffraction patterns. While other artificial intelligence (AI) agents are effective at classifying XRD data into known phases, a similarly conditioned VAE is uniquely effective at knowing what it does not know, rapidly identifying novel phases and mixtures. These capabilities demonstrate that a VAE is a valuable AI agent for materials discovery and understanding XRD measurements both on-the-fly and during post hoc analysis.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
