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Machine Learning Analysis of Literature Data on the Water Gas Shift Reaction toward Extrapolative Prediction of Novel Catalysts
| Title: | Machine Learning Analysis of Literature Data on the Water Gas Shift Reaction toward Extrapolative Prediction of Novel Catalysts |
| Authors: | Mine, Shinya Browse this author | | Jing, Yuan Browse this author | | Mukaiyama, Takumi Browse this author | | Takao, Motoshi Browse this author | | Maeno, Zen Browse this author →KAKEN DB | | Shimizu, Ken-ichi Browse this author →KAKEN DB | | Takigawa, Ichigaku Browse this author →KAKEN DB | | Toyao, Takashi Browse this author →KAKEN DB |
| Keywords: | Machine learning (ML) | | Catalysis informatics | | Water gas shift (WGS) |
| Issue Date: | 5-Mar-2022 |
| Publisher: | 日本化学会（Chemical Society of Japan） |
| Journal Title: | Chemistry letters |
| Volume: | 51 |
| Issue: | 3 |
| Start Page: | 269 |
| End Page: | 273 |
| Publisher DOI: | 10.1246/cl.210645 |
| Abstract: | 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. |
| Type: | article |
| URI: | http://hdl.handle.net/2115/85153 |
| Appears in Collections: | 触媒科学研究所 (Institute for Catalysis) > 雑誌発表論文等 (Peer-reviewed Journal Articles, etc)
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