Accurate description of crystal structures is a prerequisite for predicting the physicochemical properties of materials. However, conventional X-ray diffraction (XRD) characterization often encounters intrinsic bottlenecks when applied to complex multiphase systems, necessitating the integration of complementary optical measurement. In this study, we developed a multi-descriptor framework by integrating key parameters including space groups, Pearson symbols, and Wyckoff sequences, to categorize the dataset of over 19,000 crystals into several dozen structural prototypes. Then, an accuracy-adaptive ensemble network based on residual architectures was implemented to capture structural ``fingerprints" within phonon vibration modes and Raman spectra. The ensemble algorithm demonstrates exceptional robustness when processing various crystals of varying lengths and quality. This data-driven classification strategy not only overcomes the reliance of traditional characterization on ideal data but also provides a high-throughput tool for the automated analysis of material structures in large-scale experimental workflows.
@article{arxiv.2601.01423,
title = {Phonon-informed Crystal Structure Classification via Precision-Adaptive ResNet-based Confidence Ensemble},
author = {Hongyu Chen and Mengyu Dai and Hongjiang Chen and Ruilin Liu and Xiaole Tian and Ruixiao Lian and Yuqian Zhang and Xia Cai and Wenwu Li and Hao Zhang},
journal= {arXiv preprint arXiv:2601.01423},
year = {2026}
}