文献类型：期刊文献

英文题名：Improved Prediction for the Methane Activation Mechanism on Rutile Metal Oxides by a Machine Learning Model with Geometrical Descriptors

作者：Xu, Jiayan[1,2,3];Cao, Xiao-Ming[1,2];Hu, P.[1,2,3]

机构：[1]East China Univ Sci & Technol, Ctr Computat Chem, Key Lab Adv Mat, 130 Meilong Rd, Shanghai 200237, Peoples R China;[2]East China Univ Sci & Technol, Sch Chem & Mol Engn, Res Inst Ind Catalysis, 130 Meilong Rd, Shanghai 200237, Peoples R China;[3]Queens Univ Belfast, Sch Chem & Chem Engn, Belfast BT9 5AG, Antrim, North Ireland

年份：2019

卷号：123

期号：47

起止页码：28802

外文期刊名：JOURNAL OF PHYSICAL CHEMISTRY C

收录：;EI(收录号：20194807771778);WOS:【SCI-EXPANDED(收录号:WOS:000500417600032)】；

基金：The authors gratefully acknowledge financial support from NSFC (21673072 and 91845111) and the Program of Shanghai Subject Chief Scientist (17XD1401400).

语种：英文

外文关键词：Activation energy - Density functional theory - Methane - Titanium dioxide - Oxide minerals - Machine learning - Metals

摘要：Methane activation could occur via either the radical-like or the surface-stabilized mechanism on metal oxides. The linear Bronsted-Evans-Polanyi (BEP) relationship between activation energies and the adsorption energies of products has made it possible to swiftly predict some reaction mechanisms. However, it is not accurate enough to predict the preferential methane activation mechanism on metal oxides. Herein, to improve the prediction for the methane activation mechanism, the machine learning method percentile-LASSO was developed to extract energetic and geometrical descriptors on the basis of a series of surface-stabilized and radical-like transition states of methane activation on rutile-type metal oxides from density functional theory calculations. Revised relations are capable of classifying those two mechanisms on the same surface with a higher accuracy, which will facilitate high-throughput catalyst screening for methane activation on metal oxides.