Predicting the adsorption behavior in bulk from metal clusters

Mpourmpakis, Giannis; Stamatakis, Michail; Herrmann, Stanley; Vlachos, Dionisios G.; Andriotis, Antonis N.

doi:10.1016/j.cplett.2011.11.004

Cited by 13 publications

(16 citation statements)

References 22 publications

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“…As has been discussed in the literature, [4][5][6][7][8]32,33 the adsorption energy on the on-top site is proportional to the d-band center and GCN of the adsorbent. In Rh 405 -NP, the NO adsorption energies on the on-top sites were proportional to the d-band centers of Rh atoms in the adsorption site, where the coefficient of determination estimated by eq S1 was 0.959.…”

Section: ■ Resultsmentioning

confidence: 87%

“…The ORR activity and oxygen binding energy are expressed as a volcano-type relationship. , Therefore, tuning of binding or adsorption energy is widely accepted as one of the important measures for enhancing catalytic activity. Thus far, a good correlation between the adsorption energy and the d-band center has been achieved for on-top adsorption. − Further, it could be applied to bridge or three-fold hollow adsorptions. − The correlation between the adsorption energy and d-band center is effective only on the same adsorption site; the d-band centers calculated for each site, such as on-top, bridge, and hollow sites, correlated well with the adsorption energies for the corresponding adsorption sites, and the d-band center was discussed separately . Different correlations have been observed for different adsorption sites; thus, the development of more general descriptors is required.…”

Section: Introductionmentioning

confidence: 98%

“…The correlation between the adsorption energy on transition metals and the band centers estimated from the partial density of states has been popularly discussed since its proposal. − Atomic or molecular adsorption is a good descriptor of various catalytic reactions; consequently, this relation was discussed for various combinations of adsorbent and adsorbate, mostly using slab models. − A typical example can be found in the oxygen reduction reaction (ORR). The ORR activity and oxygen binding energy are expressed as a volcano-type relationship. , Therefore, tuning of binding or adsorption energy is widely accepted as one of the important measures for enhancing catalytic activity.…”

Section: Introductionmentioning

confidence: 99%

“…Typically, catalytic reactions occur on nanoparticles (NPs), of which surface includes ridges, vertices, possibly steps, kinks, adatoms, and defects. In many theoretical studies, − a flat surface represented by a slab model has been adopted. A nonflat surface such as a stepped surface exhibits different adsorption energy even if identical adsorption sites are considered. − The low coordination number (CN) atoms in NP shifts the d-band center upward.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

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

Nanba

Koyama

2019

J. Phys. Chem. C

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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.

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Section: ■ Resultsmentioning

confidence: 87%

Section: Introductionmentioning

confidence: 98%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

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

Nanba

Koyama

2019

J. Phys. Chem. C

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“…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.…”

Section: Introductionmentioning

confidence: 99%

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

Koyama

2021

ACS Omega

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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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Genetic Algorithms and Machine Learning for Predicting Surface Composition, Structure, and Chemistry: A Historical Perspective and Assessment

2021

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Genetic algorithms (GA) and machine learning (ML) have a long history of development and use in chemistry. Recent algorithmic and computational advances, however, have brought these methods to the forefront of chemical research, and chemistry is experiencing a transformation in the way that machines and humans interact to pursue scientific advances. The field of materials chemistry, in particular, has witnessed a considerable expansion in the maturity of GA and ML approaches, as machine-based materials design ushers in a new era of materials development, discovery, and deployment. In addition to predicting new compositions and properties of bulk materials, GA and ML have also guided new insights into the structure, composition, and chemistry of materials surfaces. In this review, we focus on how GA and ML have been used in conjunction with chemical simulation techniques to advance understanding of surface chemistry, examining the history, recent work, and overall success of these applications.

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Predicting the adsorption behavior in bulk from metal clusters

Cited by 13 publications

References 22 publications

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

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

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

Genetic Algorithms and Machine Learning for Predicting Surface Composition, Structure, and Chemistry: A Historical Perspective and Assessment

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Predicting the adsorption behavior in bulk from metal clusters

Cited by 13 publications

References 22 publications

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

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

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

Genetic Algorithms and Machine Learning for Predicting Surface Composition, Structure, and Chemistry: A Historical Perspective and Assessment

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Product

Resources

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