NO Adsorption on 4d and 5d Transition-Metal (Rh, Pd, Ag, Ir, and Pt) Nanoparticles: Density Functional Theory Study and Supervised Learning

Abstract

We studied the adsorption energies of the NO species on face-centered-cubic M 405 -nanoparticles (NPs) (M = Rh, Pd, Ag, Ir, or Pt) using density functional theory calculations. Various adsorption sites including the on-top, bridge, and hollow sites in the ridge, (100) facet, and (111) facet were considered. The molecular adsorption energy on the slab model is affected by the adsorption configurations. By contrast, we observed different NO adsorption energies even for the same configuration on the same NP's surface facet, that is, the adsorptions of NO on the bridge sites in the (111) facets were different depending on the relative location from the ridge. The band center estimated from the partial density of states was correlated with the NO adsorption energy for the on-top site. However, when all adsorption sites were considered, the band center did not correlate well with the NO adsorption energy. We performed supervised learning for the calculated NO adsorption energies on M 405 -NPs. Descriptors related to the second nearest-neighboring atoms to N and nearest-neighboring atoms to the adsorption site predicted the NO adsorption energy of all sites on M 405 -NPs together with the d-band center of atoms in the adsorption site and its standard deviation.

Biomass hydrodeoxygenation catalysts innovation from atomistic activity predictors

“…Inset in these figures there are the corresponding (multiple) correlation coefficient (R 2 ), which is used as a statistical test to measure the association between the two variables i.e. descriptors and adsorption energy [166]. As close to one is R 2 the better is the fitting and stronger the relationship between variables.…”

“…The addition of a second descriptor can increase the correlation between the chemical properties and the affinity of atomic or molecular species [165]. Using a multiple linear regression model (Y calc = β o + β 1 x 1 + β 2 x 2 ), we created combined descriptors (with x: γ , Φ, ε d and W d ; see electronic supplementary material for details on the fitting).…”

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

“…Detailed information on the construction of Pt-skin configurations is provided in ref . The vacuum space was set to >12 Å to avoid interactions with the neighboring NPs in an image cell . A cubic unit cell with a lattice parameter of 27.70 Å was used.…”

“…The number of samples for the four-fold hollow site (= eight) were insufficient for dividing the data into test and training sets during the validation of SL. A study of the NO adsorption on TM 405 -NPs revealed that the second nearest neighbor TM atoms for NO directly affected the adsorption energy on the bridge sites in the {111} facet . In addition, the buckling parameters were large for the bridge sites in the {111} facet.…”

“…To date, the band center of d -orbitals ( d -band center) estimated from the partial density states of a catalyst is regarded as a good descriptor of atomic or molecular adsorption in various catalytic reactions. − There is a volcano-type relationship between oxygen reduction reaction (ORR) activity and oxygen binding energy. − Tuning the adsorption energy or d -band center by alloying the catalyst is one of the important measures for enhancing the ORR activity of a catalyst. Recently, supervised learning (SL) studies using electronic structure descriptors, such as the d -band center, have been performed for predicting the atomic or molecular adsorption energy. − To avoid the high computational cost required for density functional theory (DFT) calculations, simplified slab models are typically used to investigate the adsorption properties of small molecules on alloy catalysts. However, the calculation in the real-system nanoparticle (NP) models is important to analyze the heterogeneity of adsorption sites in nanoparticle catalysts.…”