A Mean‐Field Model for Oxygen Reduction Electrocatalytic Activity on High‐Entropy Alloys**

Pedersen, Jack K.; Clausen, Christian M.; Skjegstad, Lars Erik J.

doi:10.1002/cctc.202200699

Cited by 5 publications

(7 citation statements)

References 35 publications

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“…We further overlay the ORR limiting potential, and estimate the catalytic activity with an Arrhenius-like expression, using the surface site fraction, a temperature of 300K, pre-exponential factor of 1, and a potential of 0.86 V vs RHE. This is similar to the estimated kinetic limited current in [ 55 ]. In the pure metal case, we assume the flat surfaces have *OH adsorption at ΔG4 {0V} = 1.06 eV and monoatomic steps have *OH adsorption at ΔG4 {0V} = 0.86 eV and assign them the fractions 1 : 3•10 -3 .…”

Section: The Oxygen Reduction and Evolution Reactionssupporting

confidence: 69%

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Scaling relations on high-entropy alloy catalyst surfaces

Hutu,

Pervolarakis,

Remediakis

et al. 2024

Preprint

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Scaling and Brøndsted-Evans-Polanai (BEP) relations have proven immensely powerful in catalysis theory. The relations provide an understanding of the Sabatier principle in a quantitative fashion, such that we can calculate the adsorption energy that most optimally compromises between a low reaction barrier and a not too strong absorption. Scaling and BEP relations are usually mapped out for pure metal surfaces and it is not directly clear how they translate to complex alloy surfaces, e.g. high-entropy alloys (HEAs). The scaling relation between *OH and *OOH is one of the most studied and best understood. Generally, both *OH and *OOH adsorb on a single surface atom, so HEAs do not change the established scaling relation, but rather widen the distribution of available adsorption energies. The situation can be different for reactions at multi-atom surface sites. The reaction between O* and *CO to form CO2 interact with more surface atoms at the initial state compared to the transition state, so for a given reaction energy, HEAs allow for lower activation energies than pure metals. The reason is that HEA surfaces can make the transition state more similar to the initial state, without the need of steps or other geometric features.

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Section: The Oxygen Reduction and Evolution Reactionssupporting

confidence: 69%

“…The two hypothetical cases have roughly the same total activity. calculated with an Arrhenius-like expression, 55 using the surface site fraction, a temperature of 300K, pre-exponential factor of 1, and a potential of 0.86 V vs RHE.…”

Section: The Oxygen Reduction and Evolution Reactionsmentioning

confidence: 99%

Scaling relations on high-entropy alloy catalyst surfaces

Hutu,

Pervolarakis,

Remediakis

et al. 2024

Preprint

View full text Add to dashboard Cite

show abstract

“…Therefore, studying entire HEA composition spaces instead of solely trying to find an optimum activity becomes more relevant. Currently, such studies include both computational work [3,[8][9][10][11][12][13][14] and laboratory experiments. [9,[15][16][17][18] In our previous work by Pedersen et al [10] , we explored the HEA compositional space using Bayesian optimization.…”

Section: Introductionmentioning

confidence: 99%

Backward Elimination: A Strategy for High-Entropy Alloy Catalyst Discovery

Mints

Pedersen

Wiberg

et al. 2022

Preprint

Self Cite

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A promising opportunity and major challenge in the field of High-Entropy Alloy (HEA) catalysis is the abundance of possible compositions. The number of possible compositions makes it impossible to study all of them. Therefore, sophisticated methods are required to intelligently select interesting compositions. On one hand, adding an element to a composition space increases the dimensionality of that space and the number of compositions within it combinatorially. However, it also increases the number of sub-spaces that are part of this larger composition space. Assuming a constant sampling density, the number of experiments required to study a large, combined composition space of sufficient dimensionality can be less than studying all of its individual sub-spaces. This hypothesis is investigated using experimental work in which 200 compositions in an 8-element composition space composed of Au, Ir, Os, Pd, Pt, Re, Rh, and Ru were synthesized as nanoparticles. Each composition was experimentally tested for the electrocatalytic activity towards the oxygen reduction reaction. The model that was constructed using this data turned out to adequately predict data in three of its 5-element composition sub-spaces. This observation paves way for a backward elimination strategy in HEA discovery. According to this strategy all elements of interest are studied in a single composition space from which knowledge of all subspaces can be learnt. Subsequently, elements that do not show a positive influence towards the studied reaction can be removed from the search space. Ultimately, this backward elimination search produces an alloy space which has a high probability of containing the most active catalyst composition.

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“…Thus, the smaller model with fewer parameters is adequate for this application and is our preferred choice based on the lower memory requirements and resulting larger batch size during inference. In Figure S4, we also show the performance of an IS2RE model applied in some of our earlier publications 9,27,31 . This is a very lean graph neural network (GNN) with only 3062 parameters so inference speed is practically instantaneous.…”

Section: Resultsmentioning

confidence: 99%

Ab Initio to Activity: Machine Learning-Assisted Optimization of High-Entropy Alloy Catalytic Activity

Clausen

Nielsen

Pedersen

2022

High Entropy Alloys & Materials

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High-entropy alloys are slowly making their debut as a platform for catalyst discovery, but conventional methods, theoretical as well as experimental, may fall short of screening the vast composition space inhabited by this class of materials. New theoretical approaches are needed to gauge the catalytic activity of high-entropy alloys and optimize the alloy composition within a feasible time frame as a prerequisite for further experimental studies. Herein, we establish a workflow for simulations of catalysis on high-entropy alloy surfaces. For each step of the modeling we present our choice of method, however, we also acknowledge that alternative options are available. We apply the developed methodology to predict the net catalytic activity of any alloy composition, within the composition space spanned by Ag-Ir-Pd-Pt-Ru, for the oxygen reduction reaction. Based on first-principle calculations, a graph convolution neural network is used to predict adsorption energies of *OH and *O. Subsequently, taking competitive co-adsorption of reaction intermediates into account, we couple the net adsorption energy distribution of a high-entropy alloy surface to the expected current density. Lastly, this procedure is used in conjunction with a Bayesian optimization scheme to search for optimal alloy compositions, which yields several promising compositions. This result shows that an unbiased in silico pre-screening and discovery of catalyst candidates is viable and will help scale the otherwise insurmountable challenge of searching for high-entropy alloy catalysts. It is our hope that our computational framework, which is freely available on GitHub, will aid other research groups to efficiently identify promising high-entropy alloy catalysts.

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A Mean‐Field Model for Oxygen Reduction Electrocatalytic Activity on High‐Entropy Alloys**

Cited by 5 publications

References 35 publications

Scaling relations on high-entropy alloy catalyst surfaces

Scaling relations on high-entropy alloy catalyst surfaces

Backward Elimination: A Strategy for High-Entropy Alloy Catalyst Discovery

Ab Initio to Activity: Machine Learning-Assisted Optimization of High-Entropy Alloy Catalytic Activity

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