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

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dc.citation.articleNumber102
dc.citation.issueNumber1
dc.citation.journalTitlenpj Computational Materials
dc.citation.volumeNumber6
dc.contributor.authorLiu, Chun-Yen
dc.contributor.authorZhang, Shijia
dc.contributor.authorMartinez, Daniel
dc.contributor.authorLi, Meng
dc.contributor.authorSenftle, Thomas P.
dc.date.accessioned2020-08-14T20:13:38Z
dc.date.available2020-08-14T20:13:38Z
dc.date.issued2020
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.
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.
dc.identifier.doihttps://doi.org/10.1038/s41524-020-00371-x
dc.identifier.urihttps://hdl.handle.net/1911/109226
dc.language.isoeng
dc.publisherSpringer Nature
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.
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.titleUsing statistical learning to predict interactions between single metal atoms and modified MgO(100) supports
dc.typeJournal article
dc.type.dcmiText
dc.type.publicationpublisher version
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