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

TL;DR

This paper demonstrates how computational modeling and machine learning can efficiently identify and optimize high entropy alloy catalysts, specifically CoMoFeNiCu, for ammonia decomposition, achieving performance comparable to expensive ruthenium catalysts.

Contribution

The study introduces a machine learning-based model to predict adsorption energies on HEA surfaces, guiding the design of cost-effective ammonia decomposition catalysts.

Findings

Co25Mo45Fe10Ni10Cu10 alloy shows high catalytic activity.

Machine learning models accurately predict adsorption energies.

Optimal alloy composition enhances nitrogen adsorption similar to ruthenium.

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, electronics, 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 catalyst. 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 NHx (x=1,3) species on CoMoFeNiCu alloy surfaces with varied alloy compositions and atomic arrangement. 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.