Optimization of High-Entropy Alloy Catalyst for Ammonia Decomposition and Ammonia Synthesis

Abstract

The successful synthesis of high-entropy alloy (HEA) nanoparticles, a long-sought goal in materials science, opens a new frontier in materials science with applications across catalysis, structural alloys, and energetic materials. Recently, a Co25Mo45­Fe10Ni10Cu10 HEA made of earth-abundant elements was shown to have a high catalytic activity for ammonia decomposition, which rivals that of state-of-the-art, but prohibitively expensive, ruthenium catalysts. Using a computational approach based on first-principles calculations in conjunction with data analytics and machine learning, we build a model to rapidly compute the adsorption energy of H, N, and NH x (x = 1, 2, 3) species on CoMoFeNiCu alloy surfaces with varied alloy compositions and atomic arrangements. We show that the 25/45 Co/Mo ratio identified experimentally as the most active composition for ammonia decomposition increases the likelihood that the surface adsorbs nitrogen equivalently to that of ruthenium while at the same time interacting moderately strongly with intermediates. Our study underscores the importance of computational modeling and machine learning to identify and optimize HEA alloys across their near-infinite materials design space.

Bayesian Optimization of High‐Entropy Alloy Compositions for Electrocatalytic Oxygen Reduction**

“…Modeling the catalytic activity of highly diverse and complex surfaces is still in its infancy with only a few studies conducted,[ 1 , 3 , 4 , 20 , 21 ] and modeling of other aspects relevant for catalysis, such as surface stability under reaction conditions, is also being investigated. [ 22 , 23 ] We propose a way to estimate the number of experiments needed using a model that has been found to correctly predict experimental trends for electrocatalytic ORR across hundreds of different alloy compositions within the Ag‐Ir‐Pd‐Pt‐Ru system.…”

“…[7,8] Combinatorial exploration of vast alloy composition spaces has been actively used as at ool in experimental catalyst discovery for av ariety of reactions and constituent elements, [9][10][11][12][13][14][15][16] and efficient sampling of catalyst materials has also progressed. [17] However,a st he number of constituent elements increases,t he number of possible compositions grows combinatorially large and individual point testing cannot be accomplished within realistic time scales (see Modeling the catalytic activity of highly diverse and complex surfaces is still in its infancy with only af ew studies conducted, [1,3,4,20,21] and modeling of other aspects relevant for catalysis,such as surface stability under reaction conditions,is also being investigated. [22,23] We propose away to estimate the number of experiments needed using am odel that has been found to correctly predict experimental trends for electrocatalytic ORR across hundreds of different alloy compositions within the Ag-Ir-Pd-Pt-Ru system.…”

Bayesian Optimization of High‐Entropy Alloy Compositions for Electrocatalytic Oxygen Reduction**

“…[7,8] Combinatorial exploration of vast alloy composition spaces has been actively used as at ool in experimental catalyst discovery for av ariety of reactions and constituent elements, [9][10][11][12][13][14][15][16] and efficient sampling of catalyst materials has also progressed. [17] However,a st he number of constituent elements increases,t he number of possible compositions grows combinatorially large and individual point testing cannot be accomplished within realistic time scales (see Modeling the catalytic activity of highly diverse and complex surfaces is still in its infancy with only af ew studies conducted, [1,3,4,20,21] and modeling of other aspects relevant for catalysis,such as surface stability under reaction conditions,is also being investigated. [22,23] We propose away to estimate the number of experiments needed using am odel that has been found to correctly predict experimental trends for electrocatalytic ORR across hundreds of different alloy compositions within the Ag-Ir-Pd-Pt-Ru system.…”

Challenges and Opportunities for Renewable Ammonia Production via Plasmon‐Assisted Photocatalysis

“…[209] On the more applied side, the study of N 2 fixation is of course among the interesting targets of a combined utilization of ML and DFT techniques, and has been used to, for example, rapidly screen single-atom catalysts, [210,211] MBenes [212] candidate catalysts for this reaction, or high-entropy alloy catalysts using fully ML techniques. [213] In the last decade, there have also been efforts to use ML to generate statistical ab initio methods, aiming to large speedups capable of sampling exceedingly large numbers of systems, such as in mapping the space of possible chemical compounds. [214,215] But it is also important to remember that ML techniques can also be fruitfully applied in tandem with experimental data, [216] shortening the exploration of the space of parameters underscoring the synthesis of novel materials, facilitating the interpretation of material characterization datasets, or guiding the sampling of catalysts candidates.…”