Disorder-Tuned B5 Step Ensembles Govern Ammonia Decomposition on Co-Cu-Fe-Mo-Ni High-Entropy Alloys

Area of Science:

• Computational materials science
• Catalysis
• Surface science

Background:

• Ammonia decomposition reaction (ADR) is crucial for sustainable ammonia production and hydrogen generation.
• Designing efficient catalysts for ADR requires understanding complex alloy surface interactions.
• Predicting alloy performance across various compositions remains a significant challenge.

Purpose of the Study:

• To develop an end-to-end computational framework for predicting alloy catalytic activity.
• To map alloy composition to surface properties and reaction rates for the ammonia decomposition reaction (ADR).
• To identify design principles for high-performance ADR catalysts based on alloy composition.

Main Methods:

• Density Functional Theory (DFT) calculations to train a cluster-expansion (CE) model.
• Metropolis Monte Carlo (MMC) simulations to model surface segregation.
• Microkinetic modeling to predict site-specific turnover frequencies (TOFs) and surface-averaged activities.
• High-throughput screening of alloy compositions.

Main Results:

• The framework accurately predicts site-resolved adsorption energies and ADR rates for Co-Cu-Fe-Mo-Ni alloys.
• Temperature-driven copper enrichment in the surface layer was observed, reducing catalytic activity.
• Cu-free Co-Fe-Mo-Ni medium-entropy alloys exhibited high, composition-robust ADR rates.
• Active sites were identified as Cu-lean and multimetallic.

Conclusions:

• The developed framework provides a powerful tool for designing efficient ADR catalysts.
• Minimizing copper content and preserving configurational disorder are key design rules for enhancing ADR activity.
• The framework is extensible to other alloy systems and reaction types (thermochemical and electrochemical).