Surface stability of perovskite oxides under OER operating conditions: a first principles approach

Raman, Abhinav S.; Patel, Rita V.; Vojvodić, Aleksandra

doi:10.1039/c9fd00146h

Cited by 28 publications

(27 citation statements)

References 36 publications

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“…Our results show that most of the facets prefer to have a high surface coverage of O*, therefore we consider mainly oxygen terminated surfaces for the OER analysis. Our results are comparable to previous studies on the electrochemical stability of IrO 2 surfaces, 58,59 but without considering highly reconstructed facets such as (101). The surface stability analysis is therefore crucial for accurate determination of the activity.…”

Section: Electrochemical Oer Applicationsupporting

confidence: 89%

Active Learning Accelerated Discovery of Stable Iridium Oxide Polymorphs for the Oxygen Evolution Reaction

et al. 2020

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The discovery of high-performing and stable materials for sustainable energy applications is a pressing goal in catalysis and materials science. Understanding the relationship between a material's structure and functionality is an important step in the process, such that viable polymorphs for a given chemical composition need to be identified. Machine-learning based surrogate models have the potential to accelerate the search for polymorphs that target specific applications. Herein, we report a readily generalizable active-learning (AL) accelerated algorithm for identification of electrochemically stable iridium-oxide polymorphs of IrO 2 and IrO 3 . The search is coupled to a subsequent analysis of the electrochemical stability of the discovered structures for the acidic oxygen evolution reaction (OER). Structural candidates are generated by identifying all 956 structurally unique AB 2 and AB 3 prototypes in existing materials databases (more than 38,000). Next, using an active learning approach we are able to find 196 IrO 2 polymorphs within the thermodynamic amorphous synthesizability limit and reaffirm the global stability of the rutile structure. We find 75 synthesizable IrO 3 polymorphs and report a previously unknown FeF 3 -type structure as the most stable, termed α-IrO 3 . To test the algorithms performance, we compare to a random search of the candidate space and report at least a twofold increase in the rate of discovery. Additionally, the AL approach can acquire the most stable polymorphs of IrO 2 and IrO 3 with less than 30 density functional theory optimizations. Analysis of the structural properties of the discovered polymorphs reveals that octahedral local coordination environments are preferred for nearly all low energy structures. Subsequent Pourbaix Ir-H 2 O analysis shows that α-IrO 3 is the globally stable solid phase under acidic OER conditions and supersedes the stability of rutile IrO 2 . Calculation of theoretical OER surface activities reveal ideal weaker binding of the OER intermediates on α-IrO 3 than on any other considered iridium-oxide. We emphasize that the proposed AL algorithm can be easily generalized to search for any binary metal-oxide structure with a defined stoichiometry.

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Section: Electrochemical Oer Applicationsupporting

confidence: 89%

Active Learning Accelerated Discovery of Stable Iridium Oxide Polymorphs for the Oxygen Evolution Reaction

et al. 2020

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“…Additionally, we have also added a kinetic OER volcano (dashed line) from Dickens et al 63 In general, the R-IrO 2 surfaces bind the OER intermediates relatively strongly, with theoretical limiting potentials of ∼1.8 V RHE (overpotential of 0.57 V RHE ) having the *O to *OOH potential limiting step, in agreement with previous theoretical studies. 59,64,65 The predicted overpotentials of our R-IrO 2 systems are also within the range of experimentally observed values (horizontal lines). 25,60 The surfaces of the three IrO 3 polymorphs have ∆G O -∆G OH values shifted to higher energies, indicative of overall weaker binding energetics (see also Figure S5).…”

Section: Electrochemical Oer Applicationsupporting

confidence: 85%

Active Learning Accelerated Discovery of Stable Iridium-oxide Polymorphs for the Oxygen Evolution Reaction

Flores

Paolucci²,

Winther³

et al. 2020

Preprint

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The discovery of high-performing and stable materials for sustainable energy applications is a pressing goal in catalysis and materials science. Understanding the relationship between a material's structure and functionality is an important step in the process, such that viable polymorphs for a given chemical composition need to be identified. Machine-learning based surrogate models have the potential to accelerate the search for polymorphs that target specific applications. Herein, we report a readily generalizable active-learning (AL) accelerated algorithm for identification of electrochemically stable iridium-oxide polymorphs of IrO2 and IrO3. The search is coupled to a subsequent analysis of the electrochemical stability of the discovered structures for the acidic oxygen evolution reaction (OER). Structural candidates are generated by identifying all 956 structurally unique AB2 and AB3 prototypes in existing materials databases (more than 38000). Next, using an active learning approach we are able to find 196 IrO2 polymorphs within the thermodynamic amorphous synthesizability limit and reaffirm the global stability of the rutile structure. We find 75 synthesizable IrO3 polymorphs and report a previously unknown FeF3-type structure as the most stable, termed α-IrO3. To test the algorithms performance, we compare to a random search of the candidate space and report at least a twofold increase in the rate of discovery. Additionally, the AL approach can acquire the most stable polymorphs of IrO2 and IrO3 with less than 30 density functional theory optimizations. Analysis of the structural properties of the discovered polymorphs reveals that octahedral local coordination environments are preferred for nearly all low energy structures. Subsequent Pourbaix Ir-H2O analysis shows that α-IrO3 is the globally stable solid phase under acidic OER conditions and supersedes the stability of rutile IrO2. Calculation of theoretical OER surface activities reveal ideal weaker binding of the OER intermediates on α-IrO3 than on any other considered iridium-oxide. We emphasize that the proposed AL algorithm can be easily generalized to search for any binary metal-oxide structure with a defined stoichiometry.

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“…It can be used to calculate reaction enthalpies and free energies (when suitably combined with entropy values), which are paramount in a wide variety of applications in chemistry and chemical engineering such as energy storage, [1,2] chemical reaction engineering, [3][4][5] process system design, [6] combustion, [7] electrocatalysis, [8][9][10] and thermochemical and electrochemical stability assessment. [10][11][12][13][14][15][16] However, experimental heats of formation are unavailable for a vast number of compounds. [17] In such cases, computational methods provide a means to calculate those and related thermochemical properties.…”

Section: Introductionmentioning

confidence: 99%

Fast Correction of Errors in the DFT‐Calculated Energies of Gaseous Nitrogen‐Containing Species

2021

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Modeling adsorption phenomena on surfaces by DFT calculations often involves substantial errors, resulting in inaccurate predictions of catalytic activities. Such errors partly stem from the inaccurate description of the energetics of free molecules. Herein, we use a semiempirical group-additivity method to correct the DFT-calculated heats of formation of 106 carbonand nitrogen-containing gaseous compounds belonging to 15 different chemical families. PBE, PW91, RPBE and BEEF-vdW initially yield mean absolute errors (MAEs) with respect to experiments in the range of 0.32-0.75 eV. After correcting the systematic errors, the overall MAEs decrease to~0.05 eV. Additionally, upon applying the corrections to three types of reaction enthalpies, the resulting MAEs are below 0.10 eV. These functional-group corrections can be used in (electro)catalysis to correct the gas-phase references necessary to evaluate equilibrium potentials and adsorption energies, predict error cancellation, and assess conflicting experimental data.

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Surface stability of perovskite oxides under OER operating conditions: a first principles approach

Abstract: The activity-stability conundrum has long been the Achilles' heel in the design of catalysts, in particular, for electrochemical reactions such as water splitting. Here, we use ab-initio thermodynamics to delineate...

Cited by 28 publications

References 36 publications

Active Learning Accelerated Discovery of Stable Iridium Oxide Polymorphs for the Oxygen Evolution Reaction

Active Learning Accelerated Discovery of Stable Iridium Oxide Polymorphs for the Oxygen Evolution Reaction

Active Learning Accelerated Discovery of Stable Iridium-oxide Polymorphs for the Oxygen Evolution Reaction

Fast Correction of Errors in the DFT‐Calculated Energies of Gaseous Nitrogen‐Containing Species

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