Anharmonic Raman spectra simulation of crystals from deep neural networks

doi:10.1063/5.0040190

	Anharmonic Raman spectra simulation of crystals from deep neural networks
	Shang, Honghui 1; Wang, Haidi 2
刊名	AIP ADVANCES
	2021-03-01
卷号	11 期号:3 页码:6
DOI	10.1063/5.0040190
英文摘要	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.
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
语种	英语
出版者	AMER INST PHYSICS
WOS记录号	WOS:000630480600005
内容类型	期刊论文
源URL	[http://119.78.100.204/handle/2XEOYT63/16872]
专题	中国科学院计算技术研究所
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
推荐引用方式 GB/T 7714	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.