XGBoost: An Optimal Machine Learning Model with Just Structural Features to Discover MOF Adsorbents of Xe/Kr-Reference-Cited by-同舟云学术

XGBoost: An Optimal Machine Learning Model with Just Structural Features to Discover MOF Adsorbents of Xe/Kr

Published:2021-03-19 Issue:13 Volume:6 Page:9066-9076
ISSN:2470-1343
Container-title:ACS Omega
language:en
Short-container-title:ACS Omega

Author:

Liang Heng¹,Jiang Kun²,Yan Tong-An³,Chen Guang-Hui¹^ORCID

Affiliation:

1. Department of Chemistry, Key Laboratory for Preparation and Application of Ordered Structural Materials of Guangdong Province, Shantou University, Shantou 515063, Guangdong, China

2. Department of Natural Science, Shantou Polytechnic, Shantou 515041, Guangdong, China

3. State Key Laboratory of Organic−Inorganic Composites, Beijing University of Chemical Technology, Beijing 100029, China

Funder

National Natural Science Foundation of China

Shantou Polytechnic

Publisher

American Chemical Society (ACS)

Subject

General Chemical Engineering,General Chemistry

Link

https://pubs.acs.org/doi/pdf/10.1021/acsomega.1c00100

Reference55 articles.

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3. Design and Characterization of Two Bifunctional Cryptophane A-Based Host Molecules for Xenon Magnetic Resonance Imaging Applications

4. Xenon for induction of anaesthesia

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