Discovery of metal–organic frameworks for inverse CO<sub>2</sub>/C<sub>2</sub>H<sub>2</sub> separation by synergizing molecular simulation and machine learning

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Publication Details

Journal
Molecular Systems Design & Engineering
Published
2025-01-01
DOI
https://doi.org/10.1039/d5me00060b
Citations
1
Primary Topic
Machine Learning in Materials Science
Type
article
Field-Weighted Citation Impact
0.52

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article

Discovery of metal–organic frameworks for inverse CO2/C2H2 separation by synergizing molecular simulation and machine learning

Daohui Zhao, Jianwen Jiang, Zhiming Zhang, Mao Wang
1 citations
Molecular Systems Design & Engineering
Machine Learning in Materials Science
0.52
article

Discovery of metal–organic frameworks for inverse CO2/C2H2 separation by synergizing molecular simulation and machine learning

Daohui Zhao, Jianwen Jiang, Zhiming Zhang, Mao Wang
article en
1 citations

Abstract

No abstract available for this paper.
Molecular Systems Design & EngineeringVol. 10(9)
National University of Singapore (SG)
Agency for Science, Technology and Research, National Research Foundation Singapore, Ministry of Education - Singapore
Openalex Percentile: Top 35%
Machine Learning in Materials Science
0.52
DOISourceOpenAlex
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Discovery of metal–organic frameworks for inverse CO<sub>2</sub>/C<sub>2</sub>H<sub>2</sub> separation by synergizing molecular simulation and machine learning — Daohui Zhao, Jianwen Jiang, et al. · Molecular Systems Design & Engineering (2025) | TGRS Research Map | TGRS