文献类型：期刊文献

英文题名：A machine-learning-assisted study of propylene adsorption behaviors on transition metals and alloys: Beyond the Dewar-Chatt-Duncanson model

作者：Wang, Yue-Xin[1];Li, Min -Hui[1];Cao, Ran[1];Lei, Ming[1];Sui, Zhi-Jun[1];Zhou, Xing-Gui[1];Chen, De[2];Zhu, Yi-An[1]

机构：[1]East China Univ Sci & Technol, Sch Chem Engn, State Key Lab Chem Engn, UNILAB, Shanghai 200237, Peoples R China;[2]Norwegian Univ Sci & Technol, Dept Chem Engn, N-7491 Trondheim, Norway

年份：2024

卷号：4

期号：2

外文期刊名：CHEM CATALYSIS

收录：WOS:【ESCI(收录号:WOS:001185210200001)】；

基金：ACKNOWLEDGMENTS This work is supported by the National Natural Science Foundation of China (91645122, 22073027, and 22278130) . The computational time provided by the No- tur project is highly acknowledged.

语种：英文

摘要：The interactions between propylene and heterogeneous catalysts play a crucial role in determining the catalytic performance in various propylene -related reactions. In this work, density functional theory (DFT) calculations and machine -learning techniques have been used to examine the adsorption behaviors of propylene on elemental transition metals and alloys. To predict propylene adsorption energies without DFT calculations, a set of intrinsic features and the random forest algorithm are employed to train a surrogate model. The analysis of frontier orbitals and density of states is then used to provide a physical interpretation of the observations by machine learning. Our results suggest the transition metal -propylene interactions are not only due to the electron transfer between the d states and the p bonding and p* antibonding orbitals in the C=C double bond, but they also are influenced by the filling and energy levels of the metal valence s and p orbitals, which is well beyond the Dewar-Chatt-Duncanson model.