Achieving vibrational energies of diatomic systems with high quality by machine learning improved DFT method-Reference-Cited by-同舟云学术

Achieving vibrational energies of diatomic systems with high quality by machine learning improved DFT method

Published:2022 Issue:55 Volume:12 Page:35950-35958
ISSN:2046-2069
Container-title:RSC Advances
language:en
Short-container-title:RSC Adv.

Author:

Yang Zhangzhang¹^ORCID,Wan Zhitao¹,Liu Li¹,Fu Jia¹,Fan Qunchao¹,Xie Feng²,Zhang Yi³,Ma Jie⁴

Affiliation:

1. School of science, Key Laboratory of High Performance Scientific Computation, Xihua University, Chengdu, 610039, China

2. 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

3. College of Advanced Interdisciplinary Studies, National University of Defense Technology, Changsha, 410073, China

4. State Key Laboratory of Quantum Optics and Quantum Optics Devices, Laser Spectroscopy Laboratory, College of Physics and Electronics Engineering, Shanxi University, Taiyuan, 030006, China

Abstract

By systematically correcting the calculation errors through machine learning, the accuracy of the diatomic vibrational energy prediction based on typical DFT methods has been improved by order of magnitude.

Funder

National Natural Science Foundation of China

Sichuan Province Science and Technology Support Program

Key Laboratory of Advanced Reactor Engineering and Safety of Ministry of Education

Publisher

Royal Society of Chemistry (RSC)

Subject

General Chemical Engineering,General Chemistry

Link

http://pubs.rsc.org/en/content/articlepdf/2022/RA/D2RA07613F

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