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

作　　者：Yurong He Kuan Lu Jinjia Liu Xinhua Gao Xiaotong Liu Yongwang Li Chunfang Huo James P.Lewis Xiaodong Wen Ning Li 

机构地区：[1]Beijing Advanced Innovation Center for Materials Genome Engineering,Beijing Information Science&Technology University,Beijing 101400,China [2]State Key Laboratory of High-efficiency Utilization of Coal and Green Chemical Engineering,College of Chemistry and Chemical Engineering,Ningxia University,Yinchuan 750021,China [3]State Key Laboratory of Coal Conversion,Institute of Coal Chemistry,Chinese Academy of Sciences,Taiyuan 030001,China [4]National Energy Center for Coal to Liquids,Synfuels China Co.,Ltd.,Beijing 101400,China [5]Hong Kong Quantum AI Laboratory,Ltd.,University of Hong Kong,Hong Kong 999077,China

出　　处：《International Journal of Minerals,Metallurgy and Materials》2023年第10期2014-2024,共11页矿物冶金与材料学报（英文版）

基　　金：financially supported from the National Natural Science Foundation of China (No.22002008);Ningxia Key Research and Development Project,China (Nos.2022BEE03002 and 2022BSB03056);funding support from Synfuels China,Co.,Ltd.and Beijing Advanced Innovation Center for Materials Genome Engineering。

摘　　要：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,Fe_(3)C,Fe_(5)C_(2),and Fe_(2)C 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 with2nd-degree polynomial feature generation predicted the best CO activation energy with a mean absolute error of 0.269 eV.

关 键 词：ADSORPTION CO activation iron carbides density functional theory 

分 类 号：O643.36[理学—物理化学]