日本表面真空学会学術講演会要旨集
Online ISSN : 2434-8589
Annual Meeting of the Japan Society of Vacuum and Surface Science 2023
セッションID: 2Fp08
会議情報

November 1, 2023
Investigating O- and OH-induced dopant segregation in single-atom alloy surfaces using density functional theory and machine learning
Anne Nicole HipolitoMarianne Ancheta PalmeroViejay OrdilloKoji ShimizuDarwin PutunganAlexandra Santos-PutunganJoey OconSatoshi WatanabeKarl Ezra PilarioAllan Abraham Padama
著者情報
会議録・要旨集 フリー

詳細
抄録

Single-atom alloy (SAA), which consists of a small amount of active metal atomically dispersed in a more inert host metal, has garnered interest as a catalyst for oxygen reduction reaction (ORR) [1]. In the past years, researchers aimed to characterize SAAs with good reactivity and stability. The stability can be analyzed using the segregation energy, which measures the preference of the dopant atom to segregate to the topmost layer of the surface. While segregation happens on pristine surfaces, it is important to note that adsorbates can also induce dopant segregation. Due to the complexity of the SAA system, identifying the significant factors influencing dopant segregation remains a challenge [2].

Hence, we investigated dopant segregation and identified the significant factors influencing it by performing density functional theory (DFT)-based calculations and machine learning (ML) methods. We generated SAA surfaces of Ag, Au, Co, Cu, Ir, Ni, Pd, Pt, and Rh and used O and OH as adsorbates, key ORR reactants. We calculated the adsorption energies and the segregation energies with and without the presence of the adsorbates. We considered a set of 44 features encompassing the elemental, energetics, and electronic properties of the SAAs. We performed a two-stage feature selection method for both O- and OH-SAA systems which reduced the features to the top five most influential – formation energies, metallic radius difference, d-band centers of the dopant at the surface, and subsurface layer, the difference in surface energy between the host and dopant atom, and difference in the total number of d-electrons between the host and dopant atom. Using these identified features, we implemented various ML models – linear regression (LR), support vector machine regression (SVR), Gaussian process regression (GPR), and extra trees regression (ETR) – to predict adsorbate-induced dopant segregation energies. We found that the SVR model, both for O-SAA and OH-SAA, exhibited the best performance among the models. For O-SAA, SVR performance metrics are R2=0.92, RMSE=0.11, and MAE=0.09 for the train set; and R2=0.94, RMSE=0.10, and MAE=0.07 for the test set while the performance metrics for OH-SAA are R2=0.81, RMSE=0.016, and MAE=0.13 for the train set; and R2=0.91, RMSE=0.13, and MAE=0.10 for the test set. Also, we identified Rh-Au(111) as a potential ORR catalyst based on the criteria – good reactivity for ORR catalysis and good stability with and without adsorbates.

References:

[1] Hannagan, R. T., Giannakakis, G., Flytzani-Stephanopoulos, M., & Sykes, E. C. H. (2020). Single-Atom Alloy Catalysis. Chemical Reviews. doi:10.1021/acs.chemrev.0c00078

[2] Butler, K. T., Davies, D. W., Cartwright, H., Isayev, O., & Walsh, A. (2018). Machine learning for molecular and materials science. Nature, 559(7715), 547–555. doi:10.1038/s41586-018-0337-2

著者関連情報
© 2023 The Japan Society of Vacuum and Surface Science
前の記事 次の記事
feedback
Top