Machine-learning-assisted high-throughput computational screening of high performance metal–organic frameworks-Reference-Cited by-同舟云学术

Machine-learning-assisted high-throughput computational screening of high performance metal–organic frameworks

Published:2020 Issue:4 Volume:5 Page:725-742
ISSN:2058-9689
Container-title:Molecular Systems Design & Engineering
language:en
Short-container-title:Mol. Syst. Des. Eng.

Author:

Shi Zenan¹²³⁴⁵^ORCID,Yang Wenyuan¹²³⁴⁵,Deng Xiaomei¹²³⁴⁵,Cai Chengzhi¹²³⁴⁵,Yan Yaling¹²³⁴⁵,Liang Hong¹²³⁴⁵,Liu Zili¹²³⁴⁵,Qiao Zhiwei¹²³⁴⁵^ORCID

Affiliation:

1. Guangzhou Key Laboratory for New Energy and Green Catalysis

2. School of Chemistry and Chemical Engineering

3. Guangzhou University

4. Guangzhou

5. P. R. China

Abstract

The combination of machine learning and high-throughput computation for the screening of MOFs with high performance.

Funder

National Natural Science Foundation of China

Science and Technology Planning Project of Guangdong Province

Publisher

Royal Society of Chemistry (RSC)

Subject

Materials Chemistry,Industrial and Manufacturing Engineering,Process Chemistry and Technology,Energy Engineering and Power Technology,Biomedical Engineering,Chemical Engineering (miscellaneous),Chemistry (miscellaneous)

Link

http://pubs.rsc.org/en/content/articlepdf/2020/ME/D0ME00005A

Reference79 articles.

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4. Recent advances in gas storage and separation using metal–organic frameworks

5. CO2 Capture and Separations Using MOFs: Computational and Experimental Studies

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