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- W4205087273 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. Literature data based on the water gas shift (WGS) reaction have been analyzed using statistical methods based on machine learning (ML). Our ML approach that considers elemental features as input representations rather than the catalyst compositions, was successfully applied, and novel promising catalyst candidates for future studies were proposed." @default.
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- W4205087273 date "2022-03-05" @default.
- W4205087273 modified "2023-09-30" @default.
- W4205087273 title "Machine Learning Analysis of Literature Data on the Water Gas Shift Reaction toward Extrapolative Prediction of Novel Catalysts" @default.
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- W4205087273 doi "https://doi.org/10.1246/cl.210645" @default.
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