| Autor: |
Shinya Mine, Yuan Jing, Takumi Mukaiyama, Motoshi Takao, Zen Maeno, Ken-ichi Shimizu, Ichigaku Takigawa, Takashi Toyao |
| Jazyk: |
angličtina |
| Rok vydání: |
2022 |
| Předmět: |
|
| Zdroj: |
Chemistry letters. 51(3):269-273 |
| ISSN: |
0366-7022 |
| Popis: |
Literature data based on the water gas shift (WGS) reaction have been analyzed using statistical methods based on machine learning (ML). Our ML approach, which considers elemental features as input representations rather than the catalyst compositions, was successfully applied, and new promising catalyst candidates for future research were proposed. |
| Databáze: |
OpenAIRE |
| Externí odkaz: |
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