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Simulation of Metal-Supported Metal-Nanoislands: A Comparison of DFT Methods.

ABSTRACT: We have evaluated various density functional theory (DFT) methods to simulate geometric, energetic, electronic, and hydrogen adsorption properties of metal-nanoparticles supported on metal surfaces. We used Pt and Pd nanoislands on Au(111) as model systems. The evaluated DFT methods include GGA (PW91, PBE, RPBE, revPBE, and PBESol), GGA with van der Waals (vdW) corrected (PBE-D3), GGA with optimized vdW functionals (revPBE-vdW), meta-GGA (SCAN and MS2), and the machine learning-based method BEEF-vdW. The results show that the various DFT methods yield similar geometric and electronic properties for Pt (or Pd) nanoislands on Au(111). The DFT methods also produce similar relative energetics for small Pt (or Pd) clusters with different conformations on Au(111). The results show that a triatomic cluster of Pt on Au(111) is more stable with a linear conformation. In contrast, a triatomic cluster of Pd is more stable with a triangular conformation. For clusters with four or more atoms, Pt and Pd clusters on Au(111) prefer non-linear conformation. We found that the various DFT methods yield different results only for the adsorption energy of hydrogen.

SUBMITTER: Vazquez-Lizardi GA

PROVIDER: S-EPMC8224827 | biostudies-literature | 2021 Oct

REPOSITORIES: biostudies-literature

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Simulation of Metal-Supported Metal-Nanoislands: A Comparison of DFT Methods.

Vázquez-Lizardi Gabriel A GA Ruiz-Casanova Louis A LA Cruz-Sánchez Ricardo M RM Santana Juan A JA

Surface science 20210610

We have evaluated various density functional theory (DFT) methods to simulate geometric, energetic, electronic, and hydrogen adsorption properties of metal-nanoparticles supported on metal surfaces. We used Pt and Pd nanoislands on Au(111) as model systems. The evaluated DFT methods include GGA (PW91, PBE, RPBE, revPBE, and PBESol), GGA with van der Waals (vdW) corrected (PBE-D3), GGA with optimized vdW functionals (revPBE-vdW), meta-GGA (SCAN and MS2), and the machine learning-based method BEEF ...[more]

PMID: 34176977

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Project description:BackgroundMultiple imputation (MI) was developed as a method to enable valid inferences to be obtained in the presence of missing data rather than to re-create the missing values. Within the applied setting, it remains unclear how important it is that imputed values should be plausible for individual observations. One variable type for which MI may lead to implausible values is a limited-range variable, where imputed values may fall outside the observable range. The aim of this work was to compare methods for imputing limited-range variables, with a focus on those that restrict the range of the imputed values.MethodsUsing data from a study of adolescent health, we consider three variables based on responses to the General Health Questionnaire (GHQ), a tool for detecting minor psychiatric illness. These variables, based on different scoring methods for the GHQ, resulted in three continuous distributions with mild, moderate and severe positive skewness. In an otherwise complete dataset, we set 33% of the GHQ observations to missing completely at random or missing at random; repeating this process to create 1000 datasets with incomplete data for each scenario.For each dataset, we imputed values on the raw scale and following a zero-skewness log transformation using: univariate regression with no rounding; post-imputation rounding; truncated normal regression; and predictive mean matching. We estimated the marginal mean of the GHQ and the association between the GHQ and a fully observed binary outcome, comparing the results with complete data statistics.ResultsImputation with no rounding performed well when applied to data on the raw scale. Post-imputation rounding and imputation using truncated normal regression produced higher marginal means than the complete data estimate when data had a moderate or severe skew, and this was associated with under-coverage of the complete data estimate. Predictive mean matching also produced under-coverage of the complete data estimate. For the estimate of association, all methods produced similar estimates to the complete data.ConclusionsFor data with a limited range, multiple imputation using techniques that restrict the range of imputed values can result in biased estimates for the marginal mean when data are highly skewed.

Dataset Information

Simulation of Metal-Supported Metal-Nanoislands: A Comparison of DFT Methods.

Publications

Simulation of Metal-Supported Metal-Nanoislands: A Comparison of DFT Methods.

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