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We studied the binding energies of O species on face-centered-cubic Pt 3 M nanoparticles (NPs) with a Pt-skin layer using density functional theory calculations, where M is Co, Ni, or Cu. It is desirable to express the property by structural parameters rather than by calculated electronic structures such as the d -band center. A generalized coordination number (GCN) is an effective descriptor to predict atomic or molecular adsorption energy on Pt-NPs. The GCN was extended to the prediction of highly active sites for oxygen reduction reaction. However, it failed to explain the O binding energies on Pt-skin Pt 150 M 51 -NPs. In this study, we introduced an element-based GCN, denoted as GCN A-B , and considered it as a descriptor for supervised learning. The obtained regression coefficients of GCN Pt-Pt were smaller than those of the other GCN A-B . With increasing M atoms in the subsurface layer, GCN Pt-M , GCN M-Pt , and GCN M-M increased. These factors could reproduce the calculated result that the O binding energies of the Pt-skin Pt 150 M 51 -NPs were less negative than those of the Pt 201 -NPs. Thus, GCN A-B explains the ligand effect of the O binding energy on the Pt-skin Pt 150 M 51 -NPs.

An Element-Based Generalized Coordination Number for Predicting the Oxygen Binding Energy on Pt3M (M = Co, Ni, or Cu) Alloy Nanoparticles

An Element-Based Generalized Coordination Number for Predicting the Oxygen Binding Energy on Pt₃M (M = Co, Ni, or Cu) Alloy Nanoparticles