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Anharmonic Raman spectra simulation of crystals from deep neural networks
Shang, Honghui1; Wang, Haidi2
2021-03-01
发表期刊AIP ADVANCES
卷号11期号:3页码:6
摘要Raman spectroscopy is an effective tool to analyze the structures of various materials as it provides chemical and compositional information. However, the computation demands for Raman spectra are typically significant because quantum perturbation calculations need to be performed beyond ground state calculations. This work introduces a novel route based on deep neural networks (DNNs) and density-functional perturbation theory to access anharmonic Raman spectra for extended systems. Both the dielectric susceptibility and the potential energy surface are trained using DNNs. The ab initio anharmonic vibrational Raman spectra can be reproduced well with machine learning and DNNs. Silicon and paracetamol crystals are used as showcases to demonstrate the computational efficiency.
DOI10.1063/5.0040190
收录类别SCI
语种英语
资助项目National Natural Science Foundation of China[22003073] ; National Natural Science Foundation of China[CARCH 4205] ; National Natural Science Foundation of China[CARCH 4411]
WOS研究方向Science & Technology - Other Topics ; Materials Science ; Physics
WOS类目Nanoscience & Nanotechnology ; Materials Science, Multidisciplinary ; Physics, Applied
WOS记录号WOS:000630480600005
出版者AMER INST PHYSICS
引用统计
被引频次:13[WOS]   [WOS记录]     [WOS相关记录]
文献类型期刊论文
条目标识符http://119.78.100.204/handle/2XEOYT63/16873
专题中国科学院计算技术研究所期刊论文_英文
通讯作者Shang, Honghui
作者单位1.Chinese Acad Sci, Inst Comp Technol, State Key Lab Comp Architecture, Beijing 100190, Peoples R China
2.Hefei Univ Technol, Sch Elect Sci & Appl Phys, Hefei 230009, Anhui, Peoples R China
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Shang, Honghui,Wang, Haidi. Anharmonic Raman spectra simulation of crystals from deep neural networks[J]. AIP ADVANCES,2021,11(3):6.
APA Shang, Honghui,&Wang, Haidi.(2021).Anharmonic Raman spectra simulation of crystals from deep neural networks.AIP ADVANCES,11(3),6.
MLA Shang, Honghui,et al."Anharmonic Raman spectra simulation of crystals from deep neural networks".AIP ADVANCES 11.3(2021):6.
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