X-ray diffraction (XRD) is an essential technique to determine a material's crystal structure in high-throughput experimentation, and has recently been incorporated in artificially intelligent agents in autonomous scientific discovery processes. However, rapid, automated and reliable analysis method of XRD data matching the incoming data rate remains a major challenge. To address these issues, we present CrystalShift, an efficient algorithm for probabilistic XRD phase labeling that employs symmetry-constrained pseudo-refinement optimization, best-first tree search, and Bayesian model comparison to estimate probabilities for phase combinations without requiring phase space information or training. We demonstrate that CrystalShift provides robust probability estimates, outperforming existing methods on synthetic and experimental datasets, and can be readily integrated into high-throughput experimental workflows. In addition to efficient phase-mapping, CrystalShift offers quantitative insights into materials' structural parameters, which facilitate both expert evaluation and AI-based modeling of the phase space, ultimately accelerating materials identification and discovery.

翻译：X射线衍射（XRD）是高通量实验中确定材料晶体结构的关键技术，近年来已被纳入自主科学发现流程中的人工智能系统。然而，匹配输入数据速率的快速、自动化且可靠的XRD数据分析方法仍是主要挑战。为解决这些问题，我们提出CrystalShift算法——一种用于概率性XRD相位标注的高效算法，该算法通过对称约束伪精化优化、最佳优先树搜索和贝叶斯模型比较，在无需相空间信息或训练的情况下估算相位组合的概率。我们证明CrystalShift能够提供稳健的概率估计，在合成和实验数据集上均优于现有方法，并可无缝集成至高通量实验工作流程。除高效的相位映射外，CrystalShift还能定量揭示材料的结构参数，有助于专家评估和基于AI的相空间建模，从而加速材料识别与发现进程。