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

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dc.citation.articleNumber102en_US
dc.citation.issueNumber1en_US
dc.citation.journalTitlenpj Computational Materialsen_US
dc.citation.volumeNumber6en_US
dc.contributor.authorLiu, Chun-Yenen_US
dc.contributor.authorZhang, Shijiaen_US
dc.contributor.authorMartinez, Danielen_US
dc.contributor.authorLi, Mengen_US
dc.contributor.authorSenftle, Thomas P.en_US
dc.date.accessioned2020-08-14T20:13:38Zen_US
dc.date.available2020-08-14T20:13:38Zen_US
dc.date.issued2020en_US
dc.description.abstractMetal/oxide interactions mediated by charge transfer influence reactivity and stability in numerous heterogeneous catalysts. In 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 support. MgO(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. We find that the charge transfer characteristics of MgO are readily modified either by adsorbates on the surface (e.g., H, OH, F, and NO2) or dopants in the oxide lattice (e.g., Li, Na, B, and Al). We use SL methods (i.e., LASSO, Horseshoe prior, and Dirichlet–Laplace prior) that are trained against DFT data to identify physical descriptors for predicting how the adsorption energy of metal atoms will change in response to support modification. These SL-derived feature selection tools are used to screen through more than one million candidate descriptors that are generated from simple chemical properties of the adsorbed metals, MgO, dopants, and adsorbates. Among the tested SL tools, we demonstrate that Dirichlet–Laplace prior predicts metal adsorption energies on MgO most accurately, while also identifying descriptors that are most transferable to chemically similar oxides, such as CaO, BaO, and ZnO.en_US
dc.identifier.citationLiu, Chun-Yen, Zhang, Shijia, Martinez, Daniel, et al.. "Using statistical learning to predict interactions between single metal atoms and modified MgO(100) supports." <i>npj Computational Materials,</i> 6, no. 1 (2020) Springer Nature: https://doi.org/10.1038/s41524-020-00371-x.en_US
dc.identifier.doihttps://doi.org/10.1038/s41524-020-00371-xen_US
dc.identifier.urihttps://hdl.handle.net/1911/109226en_US
dc.language.isoengen_US
dc.publisherSpringer Natureen_US
dc.rightsThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.en_US
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/en_US
dc.titleUsing statistical learning to predict interactions between single metal atoms and modified MgO(100) supportsen_US
dc.typeJournal articleen_US
dc.type.dcmiTexten_US
dc.type.publicationpublisher versionen_US
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