Combining Computational Modeling with Reaction Kinetics Experiments for Elucidating the <i>In Situ</i> Nature of the Active Site in Catalysis

Bhandari, Saurabh; Rangarajan, Srinivas; Mavrikakis, Manos

doi:10.1021/acs.accounts.0c00340

Cited by 74 publications

(84 citation statements)

References 76 publications

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“… 49 The most obvious structural uncertainty would be coverage effects, and future work should include a systematic approach to deal with lateral interactions. Parametric uncertainty, in contrast, should be refined with experimental results when applicable 32 or with more advanced electronic structure methods where possible, (e.g., hybrid DFT methods 110 ). Similarly, the uncertainty correlation in the species energies should be more accurately quantified than the approximate uniform distribution for a more sophisticated error propagation, 52 , 57 which can be done by using the BEEF–vdW functional 85 (see SI for further discussion).…”

Section: Resultsmentioning

confidence: 99%

“… 50 , 51 , 82 As a simplification, we do not consider uncertainty in entropy, which is present as well. 32 , 54 Although the uncertainty in the heats of formation is ±0.3 eV, there is also a strong correlation in the uncertainty between the adsorbates. 52 , 53 , 57 This correlation has led to the development of the LS concept 35 (see eq 1 ) and the breakdown of the correlated adsorbate thermochemistry space to the descriptor species C*, H*, and O*.…”

Section: Methodsmentioning

confidence: 99%

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Quantifying the Impact of Parametric Uncertainty on Automatic Mechanism Generation for CO₂ Hydrogenation on Ni(111)

Kreitz

Sargsyan

Blöndal

et al. 2021

JACS Au

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Automatic mechanism generation is used to determine mechanisms for the CO 2 hydrogenation on Ni(111) in a two-stage process while considering the correlated uncertainty in DFT-based energetic parameters systematically. In a coarse stage, all the possible chemistry is explored with gas-phase products down to the ppb level, while a refined stage discovers the core methanation submechanism. Five thousand unique mechanisms were generated, which contain minor perturbations in all parameters. Global uncertainty assessment, global sensitivity analysis, and degree of rate control analysis are performed to study the effect of this parametric uncertainty on the microkinetic model predictions. Comparison of the model predictions with experimental data on a Ni/SiO 2 catalyst find a feasible set of microkinetic mechanisms within the correlated uncertainty space that are in quantitative agreement with the measured data, without relying on explicit parameter optimization. Global uncertainty and sensitivity analyses provide tools to determine the pathways and key factors that control the methanation activity within the parameter space. Together, these methods reveal that the degree of rate control approach can be misleading if parametric uncertainty is not considered. The procedure of considering uncertainties in the automated mechanism generation is not unique to CO 2 methanation and can be easily extended to other challenging heterogeneously catalyzed reactions.

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Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Quantifying the Impact of Parametric Uncertainty on Automatic Mechanism Generation for CO₂ Hydrogenation on Ni(111)

Kreitz

Sargsyan

Blöndal

et al. 2021

JACS Au

View full text Add to dashboard Cite

show abstract

“…Likewise, TST rates are obtained and the system of differential equations corresponding to the reaction network numerically solved. This solution is within the mean‐field approximation since all species taking part in the reaction are considered randomly distributed with a homogeneous distribution of adsorbates, fast diffusion, and static active sites 173 . However, the neglect of surface heterogeneity and of diffusion‐related rate control are two aspects not considered in the microkinetic modeling that contribute to the mismatch in reaction rates between model predictions and experiments 174 .…”

Section: Bridging the Length Scales: From Micro To Macromentioning

confidence: 99%

Concepts, models, and methods in computational heterogeneous catalysis illustrated throughCO₂conversion

Morales‐García

Viñes

Gomes

et al. 2021

WIREs Comput Mol Sci

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Theoretical investigations and computational studies have notoriously contributed to the development of our understanding of heterogeneous catalysis during the last decades, when powerful computers have become generally available and efficient codes have been written that can make use of the new highly parallel architectures. The outcomes of these studies have shown not only a predictive character of theory but also provide inputs to experimentalists to rationalize their experimental observations and even to design new and improved catalysts. In this review, we critically describe the advances in computational heterogeneous catalysis from different viewpoints. We firstly focus on modeling because it constitutes the first key step in heterogenous catalysis where the systems involved are tremendously complex. A realistic description of the active sites needs to be accurately achieved to produce trustable results. Secondly, we review the techniques used to explore the potential energy landscape and how the information thus obtained can be used to bridge the gap between atomistic insight and macroscale experimental observations. This leads to the description of methods that can describe the kinetic aspects of catalysis, which essentially encompass microkinetic modeling and kinetic Monte Carlo simulations. The puissance of computer simulations in heterogeneous catalysis is further illustrated by choosing CO2 conversion catalyzed by different materials for most of which a comparison between computational information and experimental data is available. Finally, remaining challenges and a near future outlook of computational heterogeneous catalysis are provided. This article is categorized under: Structure and Mechanism > Computational Materials Science Structure and Mechanism > Reaction Mechanisms and Catalysis

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“…67 The limitations in the use of DFT or LSR-derived energies became also evident in MK modeling. 5 For instance, in formic acid decomposition on Au/SiC and Pt/C, Mavrikakis et al 68 tuned iteratively the DFT parameters by introducing coverage effects and improving the active site model until they reach the experimental values. Kinetic parameters can also be estimated employing Bayesian Statistics.…”

Section: Multi-scale Modelingmentioning

confidence: 99%

Dimensionality reduction of complex reaction networks in heterogeneous catalysis: From linear‐scalingrelationships to statistical learning techniques

Pablo‐García

Garcı́a-Muelas

Sabadell-Rendón

et al. 2021

WIREs Comput Mol Sci

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The mechanistic analysis in heterogeneous catalysis is based on listing all elementary steps and evaluating explicitly their energies. To this end, computational models based on Density Functional Theory have become a standard to estimate the information needed in mechanistic studies. Typically, either the minimum energy paths or those with the smaller span are summarized in reaction profiles. Such simplifications gather a lot of information, although further dimensionality reduction is required to obtain the most relevant descriptors of catalytic activity and generate the so‐called volcano plots. The selection of descriptors has been traditionally based on simple intermediates, such as central atoms in small molecules (as C in CH4), which have good thermodynamic correlations to other fragments containing them. Yet, in emerging processes (recent studies), the number of intermediates involved increase, configurational effects and lateral interactions become significant, and complex materials with low symmetry are employed, thus the simple rules encapsulated in linear scaling relationships lose their predictive power due to error accumulation. At the same time, large datasets generated for the intermediates call for statistical analysis and thus these techniques are being leveraged to chemical systems, particularly to reduce their dimensionality. This article is categorized under: Structure and Mechanism > Reaction Mechanisms and Catalysis Structure and Mechanism > Computational Materials Science Electronic Structure Theory > Ab Initio Electronic Structure Methods

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Combining Computational Modeling with Reaction Kinetics Experiments for Elucidating the In Situ Nature of the Active Site in Catalysis

Abstract: mean-f ield microkinetic modeling to point to the role of coordinatively unsaturated sites in catalyzing FA decomposition on Au/SiC.

Cited by 74 publications

References 76 publications

Quantifying the Impact of Parametric Uncertainty on Automatic Mechanism Generation for CO₂ Hydrogenation on Ni(111)

Quantifying the Impact of Parametric Uncertainty on Automatic Mechanism Generation for CO₂ Hydrogenation on Ni(111)

Concepts, models, and methods in computational heterogeneous catalysis illustrated throughCO₂conversion

Dimensionality reduction of complex reaction networks in heterogeneous catalysis: From linear‐scalingrelationships to statistical learning techniques

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Combining Computational Modeling with Reaction Kinetics Experiments for Elucidating the In Situ Nature of the Active Site in Catalysis

Abstract: mean-f ield microkinetic modeling to point to the role of coordinatively unsaturated sites in catalyzing FA decomposition on Au/SiC.

Cited by 74 publications

References 76 publications

Quantifying the Impact of Parametric Uncertainty on Automatic Mechanism Generation for CO2 Hydrogenation on Ni(111)

Quantifying the Impact of Parametric Uncertainty on Automatic Mechanism Generation for CO2 Hydrogenation on Ni(111)

Concepts, models, and methods in computational heterogeneous catalysis illustrated throughCO2conversion

Dimensionality reduction of complex reaction networks in heterogeneous catalysis: From linear‐scalingrelationships to statistical learning techniques

Contact Info

Product

Resources

About

Quantifying the Impact of Parametric Uncertainty on Automatic Mechanism Generation for CO₂ Hydrogenation on Ni(111)

Quantifying the Impact of Parametric Uncertainty on Automatic Mechanism Generation for CO₂ Hydrogenation on Ni(111)

Concepts, models, and methods in computational heterogeneous catalysis illustrated throughCO₂conversion