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

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.

Density Functional Theory and Machine Learning Description and Prediction of Oxygen Atom Chemisorption on Platinum Surfaces and Nanoparticles

“…To address these limitations, a more robust and complete model has been proposed using supervised learning to describe the adsorption energy between NO and 4 d- and 5 d -transitions metals, considering top, bridge, and hollow adsorption sites . Similarly, supervised learning was used to describe interactions between O atoms and bimetallic nanoparticles with a Pt skin configuration using an element-based GCN . In this study, a multidescriptor model composed of the structural and electronic properties of Pt nanoparticles and Pt surfaces was implemented to estimate the O binding energy using multiple regression analysis, a machine learning algorithm.…”

“…16 Similarly, supervised learning was used to describe interactions between O atoms and bimetallic nanoparticles with a Pt skin configuration using an elementbased GCN. 17 In this study, a multidescriptor model composed of the structural and electronic properties of Pt nanoparticles and Pt surfaces was implemented to estimate the O binding energy using multiple regression analysis, a machine learning algorithm. First, a model describing O binding to Pt surfaces alone was obtained using a linear combination of two surface geometrical features.…”

The Role of Steps on Silver Nanoparticles in Electrocatalytic Oxygen Reduction

“…The associative overpotential of Ag(322) suffered from unideal OH and OOH adsorption energies. Previous studies have demonstrated that low CN atoms decrease the Gibbs free energy via strong interactions with intermediate species [44][45][46]. However, this was not the case for OOH*.…”

Main Descriptors To Correlate Structures with the Performances of Electrocatalysts

“…Furthermore, based on the descriptor, Koper, Calle‐Vallejo, and co‐workers have built a structure‐sensitive selectivity map for acetone and CO 2 reduction [66] . Nanba and Koyama proposed an improvement to the generalized coordination number (an element‐based descriptor) to predict the O binding energy on Pt‐based alloys [67] …”