Mixed metal oxide: A new class of catalyst for methanol activation

Mehta, Shweta; Agarwal, Sheena; Kenge, Nivedita; Mekala, Siva Prasad; Patil, Vipul; Raja, Thirumalaiswamy; Joshi, Kavita

doi:10.1016/j.apsusc.2020.147449

Cited by 5 publications

(1 citation statement)

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“…[27,28,29,30,31,32,33,34,35,36,37,38,39] In our previous study of methanol activation on (220) and (311) facet of a mixed metal oxide (ZnAl 2 O 4 ) surface, we observed that changing orientation of methanol resulted in different extents of bondlength activation for its O-H bond. [40] The distance from the nearest available surface oxygen was attributed for the corresponding O-H bond activation. But at the same time a clear lack of correlation between O-H bond activation of methanol and E ads values was evident on both the surfaces.…”

Section: Introductionmentioning

confidence: 99%

Looking Beyond Adsorption Energies to Understand Interactions at Surface Using Machine Learning

Agarwal¹,

Joshi²,

Agrawal

2021

Preprint

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Abstract<br>Identifying factors that influence interactions at the surface is still an active area of research. In this study, we present the importance of analyzing bondlength activation, while interpreting Density Functional Theory (DFT) results, as yet another crucial indicator for catalytic activity. We studied the<br>adsorption of small molecules, such as O 2 , N 2 , CO, and CO 2 , on seven face-centered cubic (fcc) transition metal surfaces (M = Ag, Au, Cu, Ir, Rh, Pt, and Pd) and their commonly studied facets (100, 110, and 111). Through our DFT investigations, we highlight the absence of linear correlation between adsorption energies (E ads ) and bondlength activation (BL act ). Our study indicates the importance of evaluating both to develop a better understanding of adsorption at surfaces. We also developed a Machine Learning (ML) model trained on simple periodic table properties to predict both, E ads and BL act . Our ML model gives an accuracy of Mean Absolute Error (MAE) ∼ 0.2 eV for E ads predictions and 0.02 Å for BL act predictions. The systematic study of the ML features<br>that affect E ads and BL act further reinforces the importance of looking beyond adsorption energies to get a full picture of surface interactions with DFT.<br>

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Section: Introductionmentioning

confidence: 99%

Looking Beyond Adsorption Energies to Understand Interactions at Surface Using Machine Learning

Agarwal¹,

Joshi²,

Agrawal

2021

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

Looking beyond Adsorption Energies to Understand Interactions at Surface using Machine Learning

Agarwal

Joshi

2022

ChemistrySelect

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Identifying factors that influence interactions at the surface is still an active area of research. In this work, the importance of analyzing bond length activations (BLact) along with adsorption energies (Ea) while interpreting Density Functional Theory (DFT) results is emphasized. Investigating adsorption of different small molecules, such as O2, N2, CO, and CO2, on commonly studied facets ((100), (110), and (111)) of seven fcc transition metal surfaces (M=Ag, Au, Cu, Ir, Rh, Pt, and Pd) demonstrates the missing linear correlation between Ea and BLact. Further, tree based Machine Learning (ML) models reinforce the missing linear correlation between the two parameters and also highlight the importance of analyzing both to develop a better understanding of adsorption at surfaces. The best performing Random Forest models have a mean absolute error (MAE) of 0.19 eV for Ea prediction, and even lower MAE of 0.012 Å for BLact prediction. While often d‐band center is correlated with Ea, our observations show that infact the d‐band center has a better correlation with BLact. These observations emphasizes the role of BLact in gaining a fuller picture for catalysis. The fact that the factors responsible for BLact is a lesser‐explored subject adds to the novelty of the findings.

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First-principles-based microkinetic simulations of syngas to methanol conversion on ZnAl2O4 spinel oxide

Wang

et al. 2021

Applied Surface Science

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Mixed metal oxide: A new class of catalyst for methanol activation

Cited by 5 publications

References 83 publications

Looking Beyond Adsorption Energies to Understand Interactions at Surface Using Machine Learning

Looking Beyond Adsorption Energies to Understand Interactions at Surface Using Machine Learning

Looking beyond Adsorption Energies to Understand Interactions at Surface using Machine Learning

First-principles-based microkinetic simulations of syngas to methanol conversion on ZnAl2O4 spinel oxide

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