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Surface-Mediated Processes for Energy Production and Conversion: Critical Considerations in Model System Design for DFT Calculations
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employed in a DFT-based analysis, the active site must be well justified. An approach to address the simplification involved in the use of extended surfaces to model complicated experimental materials was recently demonstrated for the case of CO2 electroreduction on Cu.9 This approach relied on reactive force fields and machine-learned models to rapidly screen the reactivity of a vast array of active site structures and identify ones that most likely exhibit experimentally observed catalytic properties. The issue of model system choice is equally important for nonmetallic surfaces and single-atom active sites as nonuniformity in active site structures is prevalent in most explored catalysts.10,11 The issue of choosing an active site model for DFT analysis is further complicated by surface reconstruction that is often a function of operating conditions.12 For example, under conditions of the OER, significant changes to the surface composition of chalcogenides, nitrides, and phosphides occur, through surface oxidation.13,14 Further, for Ni-based crystalline oxides under OER conditions, the catalytic active phase undergoes dissolution, and the rate of this process is pHdependent.15 Thus, the choice of a model surface for DFT analysis should consider not only the structure of the most important active site but also the dynamic behavior of this site under reaction conditions.16 In addition to the choice of the active site model, it is also critical to consider how effects away from the binding site influence reactivity. For example, the coverage of adsorbates on a catalyst surface changes as a function of operating conditions, and lateral interactions between adsorbates can modify the energetics of surface reactions and control the structure of the catalyst.17−19 Further, interactions between solvents and adsorbed species can influence the energetics of surfaces processes.20,21 In this regard, it is critical for DFT calculations to consider how the reactive environment influences chemistry at the active site. From this discussion, it is clear that the active site structure and local environment in surface-catalyzed reactions are quite complex and oftentimes dynamic. While many theoretical analyses describe these effects in great detail, summarized in Figure 1, it can be daunting to explicitly include all of these effects and others that were excluded from this discussion in a model system. This means that it is important that assumptions used in the generation of model systems for DFT calculations be reasonably justified and that the influences of these assumptions on the conclusions are explicitly discussed. The discussion here focused on only
heoretical analysis of surface chemistry using Density Functional Theory (DFT) calculations has significantly contributed to our understanding of catalyzed processes related to energy production or consumption by providing performance descriptors, reaction mechanisms, and physical pictures of how electronic structure relates to catalytic reactivity.1 The identification of reactivity correlations within databases of DFT calculations has elucidated trends between active site composition (for metals, oxides, nitrides, phosphides, perovskites, etc.) or structure and catalytic reactivity or selectivity for important reactions including CO2 hydrogenation, NH3 synthesis, and the electrochemical oxygen evolution reaction (OER), among others.2−7 DFT calculations are now ubiquitously used in the analysis of catalytic processes due to the existence of easy to use software packages. It has become commonplace for publications to include combined experimental and theoretical treatment of a catalytic process, with the aim of providing a holistic picture of a reaction mechanism, an understanding of why materials exhibit their catalytic behaviors, or simply supporting inferences made from experimental measurements. While the combined approach is laudable, it is critical to recognize the challenges associated with using DFT calculations of model systems to understand experimental systems and how those challenges influence the conclusions derived from the calculations. Errors in DFT calculations are often discussed in terms of the ability of a certain exchange−correlation functional to provide an accurate representation of electronic structure and catalytic properties. It is now well documented that errors in calculated energetics of elementary surface processes and macroscopic observables, such as rate or selectivity, due to the inaccuracy of an exchange−correlation functional can be estimated.2 Furthermore, uncertainty analysis has suggested that errors when comparing macroscopic observables from DFT calculations and experimental observations go beyond errors introduced by the inaccuracy of functionals and pointed to difficulties in capturing the complexity of experimental systems in the model systems used for DFT calculations as an important source of error.8 DFT calculations of model catalytic surfaces typically use periodic structures that include only a few potential active site structures. For example, an extended (111) surface plane of an FCC metal contains top, bridge, and three-fold hollow sites. However, experimentally analyzed supported metal catalysts typically contain sites with a wide range of coordination environments that exhibit different catalytic behaviors. This comparison makes it clear that if a single model active site is © XXXX American Chemical Society
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DOI: 10.1021/acsenergylett.8b02213 ACS Energy Lett. 2018, 3, 3015−3016
Editorial
Cite This: ACS Energy Lett. 2018, 3, 3015−3016
ACS Energy Letters
Editorial
(7) Fernández, E. M.; et al. Scaling Relationships for Adsorption Energies on Transition Metal Oxide, Sulfide, and Nitride Surfaces. Angew. Chem. 2008, 120, 4761−4764. (8) Sutton, J. E.; Guo, W.; Katsoulakis, M. A.; Vlachos, D. G. Effects of Correlated Parameters and Uncertainty in Electronic-StructureBased Chemical Kinetic Modelling. Nat. Chem. 2016, 8, 331−337. (9) Huang, Y.; Chen, Y.; Cheng, T.; Wang, L.-W.; Goddard, W. A. Identification of the Selective Sites for Electrochemical Reduction of Co to C2+ Products on Copper Nanoparticles by Combining Reactive Force Fields, Density Functional Theory, and Machine Learning. ACS Energy Lett. 2018, 3, 2983. (10) Peters, B.; Scott, S. L. Single Atom Catalysts on Amorphous Supports: A Quenched Disorder Perspective. J. Chem. Phys. 2015, 142, 104708. (11) Thang, H. V.; Pacchioni, G.; DeRita, L.; Christopher, P. Nature of Stable Single Atom Pt Catalysts Dispersed on Anatase TiO2. J. Catal. 2018, 367, 104−114. (12) Matsubu, J. C.; Zhang, S.; DeRita, L.; Marinkovic, N. S.; Chen, J. G.; Graham, G. W.; Pan, X.; Christopher, P. Adsorbate-Mediated Strong Metal−Support Interactions in Oxide-Supported Rh Catalysts. Nat. Chem. 2017, 9, 120−127. (13) Jin, S. Are Metal Chalcogenides, Nitrides, and Phosphides Oxygen Evolution Catalysts or Bifunctional Catalysts? ACS Energy Lett. 2017, 2, 1937−1938. (14) Wygant, B. R.; Kawashima, K.; Mullins, C. B. Catalyst or PreCatalyst? The Effect of Oxidation on Transition Metal Carbide, Pnictide, and Chalcogenide Oxygen Evolution Catalysts. ACS Energy Lett. 2018, 2956. (15) Yang, C.; et al. Revealing Ph-Dependent Activities and Surface Instabilities for Ni-Based Electrocatalysts During the Oxygen Evolution Reaction. ACS Energy Lett. 2018, 2884−2890. (16) Reuter, K.; Scheffler, M. Composition, Structure, and Stability of RuO2 (110) as a Function of Oxygen Pressure. Phys. Rev. B: Condens. Matter Mater. Phys. 2001, 65, 035406. (17) Liu, J.; Hibbitts, D.; Iglesia, E. Dense Co Adlayers as Enablers of Co Hydrogenation Turnovers on Ru Surfaces. J. Am. Chem. Soc. 2017, 139, 11789−11802. (18) Avanesian, T.; Dai, S.; Kale, M. J.; Graham, G. W.; Pan, X.; Christopher, P. Quantitative and Atomic-Scale View of Co-Induced Pt Nanoparticle Surface Reconstruction at Saturation Coverage Via Dft Calculations Coupled with in Situ Tem and Ir. J. Am. Chem. Soc. 2017, 139, 4551−4558. (19) Getman, R. B.; Schneider, W. F.; Smeltz, A. D.; Delgass, W. N.; Ribeiro, F. H. Oxygen-Coverage Effects on Molecular Dissociations at a Pt Metal Surface. Phys. Rev. Lett. 2009, 102, 076101. (20) Sievers, C.; Noda, Y.; Qi, L.; Albuquerque, E. M.; Rioux, R. M.; Scott, S. L. Phenomena Affecting Catalytic Reactions at Solid−Liquid Interfaces. ACS Catal. 2016, 6, 8286−8307. (21) Resasco, J.; Chen, L. D.; Clark, E.; Tsai, C.; Hahn, C.; Jaramillo, T. F.; Chan, K.; Bell, A. T. Promoter Effects of Alkali Metal Cations on the Electrochemical Reduction of Carbon Dioxide. J. Am. Chem. Soc. 2017, 139, 11277−11287. (22) Reuter, K.; Plaisance, C. P.; Oberhofer, H.; Andersen, M. Perspective: On the Active Site Model in Computational Catalyst Screening. J. Chem. Phys. 2017, 146, 040901.
Figure 1. Schematic showing various factors that should be considered in the design of model systems for DFT calculations. J. Resasco is acknowledged for making the figure.
considerations in model system design and ignored an equally critical issue of ensuring that all necessary elementary steps are included in theoretical analyses. This Editorial is not meant to dissuade or criticize the computational analysis of surface chemistry; rather, it is meant to highlight critical considerations in designing model systems for DFT calculations that facilitate deeper relationships between experiment and theory and ultimately deeper insights into the working mechanisms and approaches for design of catalytic processes.22
Phillip Christopher*
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Department of Chemical Engineering, University of California, Santa Barbara, Santa Barbara, California 93117, United States
AUTHOR INFORMATION
Corresponding Author
*E-mail:
[email protected]. ORCID
Phillip Christopher: 0000-0002-4898-5510 Notes
Views expressed in this editorial are those of the author and not necessarily the views of the ACS. The author declares no competing financial interest.
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REFERENCES
(1) Nørskov, J. K.; Bligaard, T.; Rossmeisl, J.; Christensen, C. H. Towards the Computational Design of Solid Catalysts. Nat. Chem. 2009, 1, 37−46. (2) Medford, A. J.; Wellendorff, J.; Vojvodic, A.; Studt, F.; AbildPedersen, F.; Jacobsen, K. W.; Bligaard, T.; Nørskov, J. K. Assessing the Reliability of Calculated Catalytic Ammonia Synthesis Rates. Science 2014, 345, 197−200. (3) Abild-Pedersen, F.; Greeley, J.; Studt, F.; Rossmeisl, J.; Munter, T. R.; Moses, P. G.; Skúlason, E.; Bligaard, T.; Nørskov, J. K. Scaling Properties of Adsorption Energies for Hydrogen-Containing Molecules on Transition-Metal Surfaces. Phys. Rev. Lett. 2007, 99, 016105. (4) Studt, F.; Sharafutdinov, I.; Abild-Pedersen, F.; Elkjær, C. F.; Hummelshøj, J. S.; Dahl, S.; Chorkendorff, I.; Nørskov, J. K. Discovery of a Ni-Ga Catalyst for Carbon Dioxide Reduction to Methanol. Nat. Chem. 2014, 6, 320−324. (5) Calle-Vallejo, F.; Loffreda, D.; Koper, M. T. M.; Sautet, P. Introducing Structural Sensitivity into Adsorption−Energy Scaling Relations by Means of Coordination Numbers. Nat. Chem. 2015, 7, 403−410. (6) Man, I. C.; Su, H.-Y.; Calle-Vallejo, F.; Hansen, H. A.; Martínez, J. I.; Inoglu, N. G.; Kitchin, J.; Jaramillo, T. F.; Nørskov, J. K.; Rossmeisl, J. Universality in Oxygen Evolution Electrocatalysis on Oxide Surfaces. ChemCatChem 2011, 3, 1159−1165. 3016
DOI: 10.1021/acsenergylett.8b02213 ACS Energy Lett. 2018, 3, 3015−3016
