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Volume 30 Issue 10
Oct.  2023
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文章导航 >  矿物冶金与材料学报（英文）  > 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
Cite this article as:
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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研究论文

结合键价和体相电荷加速预测碳化铁上C–O键解离活化能

  • 1)

    北京信息科技大学材料基因工程高精尖创新中心, 北京 101400, 中国

  • 2)

    宁夏大学化学化工学院(省部共建煤炭高效利用与绿色化工国家重点实验室), 银川 750021, 中国

  • 3)

    中国科学院山西煤炭化学研究所, 太原 030001, 中国

  • 4)

    中科合成油技术有限公司, 北京 101400, 中国

  • 5)

    香港科技大学香港量子人工智能实验室有限公司, 香港 999077, 中国




  • 通讯作者:

    刘晓彤    E-mail: liuxiaotong@bistu.edu.cn

    霍春芳    E-mail: huochunfang@synfuelschina.com.cn

    James P. Lewis    E-mail: james.p.lewis.phd@gmail.com

文章亮点

  • （1）提出吸附态键价结合体相电荷的CO吸附结构描述因子。
  • （2）通过吸附态结构快速准确预测CO解离活化能。
  • （3）揭示体相结构对表面反应的作用本质。
  • CO分子在铁基材料上的活化是许多化学过程的关键基元反应。我们通过密度泛函理论计算，研究了CO在一系列Fe、Fe3C、Fe5C2和Fe2C催化剂上的吸附和解离。我们发现在不同的表面和位点上CO的吸附和活化性能有很大的不同。CO的活化取决于分子与表面的局部配位环境和催化剂的体相性质。体相性质和不同的局部键合环境的差异导致吸附的CO分子与表面之间不同的相互作用，从而产生不同的C–O键活化水平。我们还通过线性回归模型进行了不同类型铁基催化剂上吸附的CO活化能预测。我们结合来自表面和体相的特征来增强活化能的预测，并利用多项式表示的特征工程进行了八种不同的线性回归。其中，采用2次多项式特征生成的脊状线性回归模型预测CO的活化能表现最佳，平均绝对误差为0.269 eV。
    • Research Article

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

    + Author Affiliations
      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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