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

We studied the binding energies of O species on face-centered-cubic Pt3M 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 Pt150M51-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 Pt150M51-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 Pt150M51-NPs.

Automated summary

The binding energies of O species on face-centered-cubic Pt3M nanoparticles with a Pt-skin layer were studied using density functional theory calculations. The generalized coordination number (GCN) was used as an effective descriptor to predict atomic or molecular adsorption energy on Pt-NPs. An element-based GCN, referred to as GCN(A-B), was introduced and considered as a descriptor for supervised learning. The obtained regression coefficients of GCN(Pt-)(Pt) were smaller than those of the other GCN(A-B), indicating its role in explaining the ligand effect of the O binding energy on the Pt-skin Pt150M51-NPs.