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231225s2021 xx |||||o 00| ||eng c |
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|a 10.1002/jcc.26554
|2 doi
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|a eng
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| 100 |
1 |
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|a Ishioka, Sora
|e verfasserin
|4 aut
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| 245 |
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|a Unveiling gas-phase oxidative coupling of methane via data analysis
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|c 2021
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|a Text
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|2 rdacontent
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|a ƒaComputermedien
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|a ƒa Online-Ressource
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|a Date Revised 16.06.2021
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|a published: Print-Electronic
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|a Citation Status PubMed-not-MEDLINE
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|a © 2021 Wiley Periodicals LLC.
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|a Unveiling the details of the mechanisms of a chemical reaction is a difficult task as reaction mechanisms are strongly coupled with reaction conditions. Here, catalysts informatics combined with high-throughput experimental data is implemented to understand the oxidative coupling of methane (OCM) reaction. In particular, pairwise correlation and data visualization are performed to reveal the relation between reaction conditions and selectivity/conversion. In addition, machine learning is used to fill the gap between experimental data points; thus, a more detailed understanding of the OCM reaction against reaction conditions can be achieved. Therefore, catalysts informatics is proposed for understanding the details of the reaction mechanism, thereby aiding reaction design
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| 650 |
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|a Journal Article
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|a Miyazato, Itsuki
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Takahashi, Lauren
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Nguyen, Thanh Nhat
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Taniike, Toshiaki
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Takahashi, Keisuke
|e verfasserin
|4 aut
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| 773 |
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|i Enthalten in
|t Journal of computational chemistry
|d 1984
|g 42(2021), 20 vom: 30. Juli, Seite 1447-1451
|w (DE-627)NLM098138448
|x 1096-987X
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|g volume:42
|g year:2021
|g number:20
|g day:30
|g month:07
|g pages:1447-1451
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|u http://dx.doi.org/10.1002/jcc.26554
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