Data-driven machine learning model for the prediction of oxygen vacancy formation energy of metal oxide materials-Reference-Cited by-同舟云学术

Data-driven machine learning model for the prediction of oxygen vacancy formation energy of metal oxide materials

Author:

Wan Zhongyu¹²³⁴⁵^ORCID,Wang Quan-De¹²³⁴^ORCID,Liu Dongchang⁵⁶⁷⁸,Liang Jinhu⁹¹⁰¹¹⁴

Affiliation:

1. Low Carbon Energy Institute and School of Chemical Engineering

2. China University of Mining and Technology

3. Xuzhou

4. People's Republic of China

5. Department of Physics

6. Sungkyunkwan University

7. Suwon 16419

8. Korea

9. School of Environment and Safety Engineering

10. North University of China

11. Taiyuan 030051

Abstract

A novel approach for the prediction of oxygen vacancy formation energy of metal oxides is established.

Funder

Fundamental Research Funds for the Central Universities

Publisher

Royal Society of Chemistry (RSC)

Subject

Physical and Theoretical Chemistry,General Physics and Astronomy

Link

Reference45 articles.

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