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.

结晶位移提供了一种高效的算法，利用对称约束伪交效优化，最佳优先搜索，贝叶斯模型比较等方法，无需阶段空间信息或训练即可估计相位组合的概率，并提供材料结构参数的定量洞察，最终加速材料识别和发现。