A data- and model-driven strategy for the evaluation of the experimental transition lines: Theoretical prediction for the ground state of <sup>12</sup>C<sup>16</sup>O.

Fan, Zhixiang; He, JieJie; Ni, Zhizhang; Fan, Qunchao; Fu, Jia; Xu, Yonggen; Li, Huidong; Ma, Jie; Xie, Feng

A data- and model-driven strategy for the evaluation of the experimental transition lines: Theoretical prediction for the ground state of ¹²C¹⁶O.

Fan, Zhixiang; He, JieJie; Ni, Zhizhang; Fan, Qunchao; Fu, Jia; Xu, Yonggen; Li, Huidong; Ma, Jie; Xie, Feng.

Afiliación

Fan Z; School of Science, Key Laboratory of High Performance Scientific Computation, Xihua University, Chengdu 610039, China.
He J; School of Science, Key Laboratory of High Performance Scientific Computation, Xihua University, Chengdu 610039, China.
Ni Z; School of Science, Key Laboratory of High Performance Scientific Computation, Xihua University, Chengdu 610039, China.
Fan Q; School of Science, Key Laboratory of High Performance Scientific Computation, Xihua University, Chengdu 610039, China. Electronic address: fanqunchao@mail.xhu.edu.cn.
Fu J; School of Science, Key Laboratory of High Performance Scientific Computation, Xihua University, Chengdu 610039, China. Electronic address: fujiayouxiang@126.com.
Xu Y; School of Science, Key Laboratory of High Performance Scientific Computation, Xihua University, Chengdu 610039, China.
Li H; School of Science, Key Laboratory of High Performance Scientific Computation, Xihua University, Chengdu 610039, China.
Ma J; State Key Laboratory of Quantum Optics and Quantum Optics Devices, Laser Spectroscopy Laboratory, College of Physics and Electronics Engineering, Shanxi University, Taiyuan 030006, China.
Xie F; Institute of Nuclear and New Energy Technology, Collaborative Innovation Center of Advanced Nuclear Energy Technology, Key Laboratory of Advanced Reactor Engineering and Safety of Ministry of Education, Tsinghua University, Beijing 100084, China.

Spectrochim Acta A Mol Biomol Spectrosc ; 264: 120278, 2022 Jan 05.

Article en En | MEDLINE | ID: mdl-34438116

Palabras clave

Carbon monoxide; Einstein A coefficient; Line identification; Line intensity; Machine learning; Oscillator strength

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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Tipo de estudio: Prognostic_studies / Risk_factors_studies Idioma: En Revista: Spectrochim Acta A Mol Biomol Spectrosc Asunto de la revista: BIOLOGIA MOLECULAR Año: 2022 Tipo del documento: Article País de afiliación: China Pais de publicación: Reino Unido

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