Abstract
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 GCNA–B, and considered it as a descriptor for supervised learning. The obtained regression coefficients of GCNPt–Pt were smaller than those of the other GCNA–B. With increasing M atoms in the subsurface layer, GCNPt–M, GCNM–Pt, and GCNM–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 Pt201-NPs. Thus, GCNA–B explains the ligand effect of the O binding energy on the Pt-skin Pt150M51-NPs.
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