## 1 Introduction
This situation highlights the dual paradox of modern XRD analysis: on one hand, phase identification based on PDF card databases remains an important standard for routine laboratory analysis [11]; on the other hand, the inadequate ability to identify substances outside the database has severely constrained the efficiency of new material development. The application of machine learning in XRD analysis has alleviated two critical constraints inherent to conventional approaches: the heavy reliance on well-curated PDF card databases and the prerequisite knowledge of initial structural models. Machine learning methods differ from manually preset features such as peak positions and intensities (like the \"three strongest peaks\" rule in PDF cards) by automatically learning higher-order features through the training of convolutional operators or attention mechanisms on patterns information. In recent scientific research, various machine learning algorithms have demonstrated their powerful data-driven processing capabilities [12]. [PERSON] et al. [13] proposed a machine learning framework for materials discovery and characterization using X-ray scattering data. [PERSON] et al. [14] proposed an AI-enhanced framework for real-time mineral phase identification and quantification using X-ray diffraction analysis. [PERSON] et al. [15] discussed in detail a series of advancements in the application of machine learning in the field of XRD data analysis. Specifically, support vector machines (SVMs), convolutional neural networks (CNNs), random forests (RFs), k-nearest neighbors (KNN),decision trees (DTs), gradient boosting, and naive Bayes (NB) have shown advantages in scenarios such as feature extraction from XRD images (e.g., synchrotron radiation data stream analysis [16]), anomaly detection [17], crystal system classification (recognition of seven crystal systems [18; 19]), identification of perovskite and non-perovskite materials [20], and classification of geothermal rock minerals [21]. In the field of atomic structure modeling of crystals, machine learning methods are being widely applied to crystal structure analysis, including cubic lattice structure classification, space group and crystal symmetry prediction, and phase transition analysis. Techniques such as dynamic time warping (DTW), autoencoders, extremely randomized trees (ERTs), and deep neural networks (DNNs) have shown unique advantages in these tasks. For example, [PERSON] et al. [22] proposed a neural network-based method for classifying crystal symmetry from X-ray diffraction patterns, which can effectively identify cubic lattice structures. Similarly, [PERSON] et al. [23] achieved successful prediction of crystal space groups and symmetries from X-ray diffraction data using an interpretable machine learning method. These studies indicate that machine learning methods have significant potential in handling complex crystallographic data. Moreover, [PERSON] et al. [24] utilized unsupervised machine learning techniques to address the big data processing issues in modern X-ray diffraction data, providing new tools for phase transition analysis. [PERSON] et al. [25] successfully distinguished the composition of ThMn12-type alloys through machine learning clustering techniques. In terms of feature extraction, autoencoders have been proven to be an effective tool, capable of constructing feature spaces from XRD patterns [26]. In recent years, the paradigm of self-supervised learning, particularly contrastive learning, has seen rapid development in materials science, with its unique multimodal mutual learning model and strong zero-shot capabilities significantly enhancing the perception of physical information [27]. The combination of contrastive learning with XRD structural analysis has made it possible to break through the paradigm from \"hard classification\" to \"exploration of the correlation between structural and spectroscopic information\".