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arXiv · 2601.01423

Phonon-informed Crystal Structure Classification via Precision-Adaptive ResNet-based Confidence Ensemble

Hongyu Chen·Mengyu Dai·Hongjiang Chen·Ruilin Liu·Xiaole Tian·Ruixiao Lian·Yuqian Zhang·Xia Cai·Wenwu Li·Hao Zhang
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Abstract

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.

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Hongyu Chen, Mengyu Dai, Hongjiang Chen, Ruilin Liu, Xiaole Tian, Ruixiao Lian, Yuqian Zhang, Xia Cai, Wenwu Li, Hao Zhang. 2026-01-04. Phonon-informed Crystal Structure Classification via Precision-Adaptive ResNet-based Confidence Ensemble. https://arxiv.org/abs/2601.01423

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