  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.
