Using statistical learning to predict interactions between single metal atoms and modified MgO(100) supports

作     者：Chun-Yen Liu Shijia Zhang Daniel Martinez Meng Li Thomas P.Senftle 

作者机构：Department of Chemical and Biomolecular EngineeringRice UniversityHoustonTX 77005USA Department of Computer Science and EngineeringSchool of Electrical Engineering and Computer SciencePennsylvania State UniversityUniversity ParkPA 16802USA Department of StatisticsRice UniversityHoustonTX 77005USA 

出 版 物：《npj Computational Materials》 (计算材料学（英文）)

年 卷 期：2020年第6卷第1期

页      面：811-823页

核心收录：

学科分类：081704[工学-应用化学] 07[理学] 070304[理学-物理化学(含∶化学物理)] 08[工学] 0817[工学-化学工程与技术] 0703[理学-化学] 

基　　金：Rice University 

主　　题：reactivity adsorption stability 

摘      要：Metal/oxide interactions mediated by charge transfer influence reactivity and stability in numerous heterogeneous *** this work,we use density functional theory(DFT)and statistical learning(SL)to derive models for predicting how the adsorption strength of metal atoms on MgO(100)surfaces can be enhanced by modifications of the ***(100)in its pristine form is relatively unreactive,and thus is ideal for examining ways in which its electronic interactions with metals can be enhanced,tuned,and controlled.