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Decoding Phases of Matter by Machine-Learning Raman Spectroscopy
Cui, Anyang; Jiang, Kai; Jiang, Minhong; Shang, Liyan; Zhu, Liangqing; Hu, Zhigao; Xu, Guisheng; Chu, Junhao
2019-11-21
Source PublicationPHYSICAL REVIEW APPLIED
ISSN2331-7019
Volume12Issue:5
SubtypeArticle
AbstractPhase transitions of condensed matter have long been a spotlight issue studied by extensive theoretical and experimental investigations. Machine learning can build an integral model-dominant workflow to statistically analyze the collective dynamics of materials and deduce the structure. We use a supportvector-machine algorithm to propose an effective method to recognize the orthorhombic, tetragonal, and cubic phases as well as to construct the phase diagram in ferroelectric crystals by mining and learning the behavioral vectors of the phonon vibrations in a crystalline lattice from Raman scattering, which is a tool typically used to detect structural properties at the molecular level. This study creates a unifying framework including material synthesis and characterization, feature engineering and principal-component analysis, learner evaluation and optimization, structure prediction, and future development of the model. It paves the way to the application of a generic approach for predicting unexplored structures and materials in the future.
DOI10.1103/PhysRevApplied.12.054049
WOS KeywordLEAD-FREE ; PIEZOELECTRIC PROPERTIES ; SINGLE-CRYSTAL ; DESIGN ; TRANSITIONS ; PROPERTY
Language英语
WOS Research AreaPhysics
PublisherAMER PHYSICAL SOC
Citation statistics
Document Type期刊论文
Identifierhttp://ir.sic.ac.cn/handle/331005/27484
Collection中国科学院上海硅酸盐研究所
Recommended Citation
GB/T 7714
Cui, Anyang,Jiang, Kai,Jiang, Minhong,et al. Decoding Phases of Matter by Machine-Learning Raman Spectroscopy[J]. PHYSICAL REVIEW APPLIED,2019,12(5).
APA Cui, Anyang.,Jiang, Kai.,Jiang, Minhong.,Shang, Liyan.,Zhu, Liangqing.,...&Chu, Junhao.(2019).Decoding Phases of Matter by Machine-Learning Raman Spectroscopy.PHYSICAL REVIEW APPLIED,12(5).
MLA Cui, Anyang,et al."Decoding Phases of Matter by Machine-Learning Raman Spectroscopy".PHYSICAL REVIEW APPLIED 12.5(2019).
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