Phonon-informed Crystal Structure Classification via Precision-Adaptive ResNet-based Confidence Ensemble
Fuente:
arXiv
Salvato in:
| Autori principali: | , , , , , , , , , |
|---|---|
| Natura: | Preprint |
| Pubblicazione: |
2026
|
| Soggetti: | |
| Accesso online: | |
| Tags: |
Aggiungi Tag
Nessun Tag, puoi essere il primo ad aggiungerne!!
|
| _version_ | 1866911353155878912 |
|---|---|
| author | Chen, Hongyu Dai, Mengyu Chen, Hongjiang Liu, Ruilin Tian, Xiaole Lian, Ruixiao Zhang, Yuqian Cai, Xia Li, Wenwu Zhang, Hao |
| author_facet | Chen, Hongyu Dai, Mengyu Chen, Hongjiang Liu, Ruilin Tian, Xiaole Lian, Ruixiao Zhang, Yuqian Cai, Xia Li, Wenwu Zhang, Hao |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_01423 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Phonon-informed Crystal Structure Classification via Precision-Adaptive ResNet-based Confidence Ensemble Chen, Hongyu Dai, Mengyu Chen, Hongjiang Liu, Ruilin Tian, Xiaole Lian, Ruixiao Zhang, Yuqian Cai, Xia Li, Wenwu Zhang, Hao Materials Science 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. |
| title | Phonon-informed Crystal Structure Classification via Precision-Adaptive ResNet-based Confidence Ensemble |
| topic | Materials Science |
| url | https://arxiv.org/abs/2601.01423 |