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Volume 30 Issue 10
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Article Navigation >  International Journal of Minerals, Metallurgy and Materials  > 2023  >  30(10): 2014-2024
Yurong He, Kuan Lu, Jinjia Liu, Xinhua Gao, Xiaotong Liu, Yongwang Li, Chunfang Huo, James P. Lewis, Xiaodong Wen,  and Ning Li, Speeding up the prediction of C–O cleavage through bond valence and charge on iron carbides, Int. J. Miner. Metall. Mater., 30(2023), No. 10, pp. 2014-2024. https://doi.org/10.1007/s12613-023-2612-y
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Yurong He, Kuan Lu, Jinjia Liu, Xinhua Gao, Xiaotong Liu, Yongwang Li, Chunfang Huo, James P. Lewis, Xiaodong Wen,  and Ning Li, Speeding up the prediction of C–O cleavage through bond valence and charge on iron carbides, Int. J. Miner. Metall. Mater., 30(2023), No. 10, pp. 2014-2024. https://doi.org/10.1007/s12613-023-2612-y
shu
Research Article

Speeding up the prediction of C–O cleavage through bond valence and charge on iron carbides

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    Abstract
  • The activation of CO on iron-based materials is a key elementary reaction for many chemical processes. We investigate CO adsorption and dissociation on a series of Fe, Fe3C, Fe5C2, and Fe2C catalysts through density functional theory calculations. We detect dramatically different performances for CO adsorption and activation on diverse surfaces and sites. The activation of CO is dependent on the local coordination of the molecule to the surface and on the bulk phase of the underlying catalyst. The bulk properties and the different local bonding environments lead to varying interactions between the adsorbed CO and the surface and thus yielding different activation levels of the C–O bond. We also examine the prediction of CO adsorption on different types of Fe-based catalysts by machine learning through linear regression models. We combine the features originating from surfaces and bulk phases to enhance the prediction of the activation energies and perform eight different linear regressions utilizing the feature engineering of polynomial representations. Among them, a ridge linear regression model with 2nd-degree polynomial feature generation predicted the best CO activation energy with a mean absolute error of 0.269 eV.
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