知识驱动的XRD物相识别：基于多视角检索与解释
Knowledge-Driven XRD Phase Identification via Multi-View Retrieval and Explanation
浏览论文内容
中文总结 AI 辅助
提出一个多决策XRD物相识别框架，结合表示学习、相似性检索和可解释决策，在合成数据上实现晶体系统98.85%和空间群95.82%的准确率，支持高通量材料发现。
中文摘要 AI 辅助
X射线衍射（XRD）是一种用于确定晶体材料物相组成和结构的实验技术。然而，解释XRD图谱具有挑战性，特别是在高通量材料发现中，许多新材料可能需要表征，且没有参考图谱可用。因此，机器学习越来越多地被用于加速和自动化分析，同时减少与人工解释相关的错误。我们提出了一种用于XRD物相分析的多决策框架，该框架在统一的参考数据库内整合了表示学习、基于相似性的检索和可解释的决策支持。卷积自编码器学习XRD图谱的紧凑潜在表示，这些表示保留了结构相似性，同时对实验噪声和测量条件引起的变化保持鲁棒性。通过在共享潜在空间内整合多条决策路径，该框架超越了单标签预测，转向排序的和可解释的物相分析，这反映了专家的实践。在推理过程中，应用互补的决策机制，包括潜在空间分类和检索、使用集成梯度的解释引导相似性以及基于成分的相似性搜索。这些机制生成排序的候选物相列表，这些列表被聚合为带有相关置信度分数的最终预测。在合成数据集上的实验证明了强大的预测性能，在测试集上晶体系统分类准确率达到98.85%，空间群预测准确率达到95.82%，同时在现实扰动下保持鲁棒性。该框架支持在高通量和探索性材料发现环境中对晶体物相和结构进行可靠、分析者友好的识别。
英文摘要
X-ray diffraction (XRD) is a experimental technique for determining the phase composition and structure of crystalline materials. However, interpreting XRD patterns is challenging, particularly in high-throughput materials discovery, where many novel materials may need to be characterized and no reference patterns are available. Consequently, machine learning is increasingly used to accelerate and automate the analysis while reducing errors associated with human interpretation. We propose a multi-decision framework for XRD phase analysis that integrates representation learning, similarity-based retrieval, and explainable decision support within a unified reference database. A convolutional autoencoder learns compact latent representations of XRD patterns that preserve structural similarity while remaining robust to variations arising from experimental noise and measurement conditions. By integrating multiple decision pathways within a shared latent space, the framework moves beyond single-label prediction toward ranked and interpretable phase analysis that mirrors expert practice. During inference, complementary decision mechanisms are applied, including latent-space classification and retrieval, explanation-guided similarity using Integrated Gradients, and composition-based similarity search. These mechanisms generate ranked candidate phase lists that are aggregated into a final prediction with an associated confidence score. Experiments on synthetic datasets demonstrate strong predictive performance, achieving 98.85\,\% accuracy for crystal system classification and 95.82\,\% accuracy for space group prediction on the test set, while maintaining robustness under realistic perturbations. The framework supports reliable, analyst-friendly identification of crystal phases and structures in high-throughput and exploratory materials discovery settings.
发表机构
- Interdisciplinary Centre for Advanced Materials Simulation（先进材料模拟跨学科中心）
- Ruhr University Bochum（波鸿鲁尔大学）
机构由 AI 辅助整理，请以论文原文为准。
