X-ray diffraction (XRD) is used for characterizing the crystal structure of molecular sieves after synthetic experiments. However, for a high-throughput molecular sieve synthetic system, the huge amount of data derived from large throughput capacity makes it difficult to analyze timely. While the kernel step of XRD analysis is to search peaks, an automatic way for peak search is needed. Thus, we proposed a novel semantic mask-based two-step framework for peak search in XRD patterns: (1) mask generation, we proposed a multi-resolution net (MRN) to classify the data points of XRD patterns into binary masks (peak/background). (2) Peak search, based on the generated masks, the background points are used to fit an n-order polynomial background curve and estimate the random noises in XRD patterns. Then we proposed three rules named mask, shape, and intensity to screening peaks from initial peak candidates generated by maximum search. Besides, a voting strategy is proposed in peak screening to obtain a precise peak search result. Experiments show that the proposed MRN achieves the state-of-the-art performance compared with other semantic segmentation methods and the proposed peak search method performs better than Jade when using f1 score as the evaluation index.

X射线衍射（XRD）用于在合成实验后表征分子筛的晶体结构。然而，对于高通量分子筛合成系统来说，大通量带来的海量数据导致难以及时分析。虽然 XRD 分析的核心步骤是搜索峰，但需要一种自动搜索峰的方法。因此，我们提出了一种新颖的基于语义掩模的两步框架，用于 XRD 图案中的峰值搜索：（1）掩模生成，我们提出了一种多分辨率网络（MRN），将 XRD 图案的数据点分类为二进制掩模（峰值） /背景）。 (2)峰值搜索，基于生成的掩模，使用背景点来拟合n阶多项式背景曲线并估计XRD图案中的随机噪声。然后，我们提出了三个规则，即掩模、形状和强度，以从最大搜索生成的初始候选峰中筛选峰。此外，在峰值筛选中提出了投票策略，以获得精确的峰值搜索结果。实验表明，与其他语义分割方法相比，所提出的 MRN 实现了最先进的性能，并且当使用 f1 分数作为评估指标时，所提出的峰值搜索方法比 Jade 表现更好。