journal article·2026-07-18Open access

Annealing-aware screening of Co–Ir–Ni–Rh–Ru alloys for ammonia decomposition

Xingyu Li, Cao Wang, Liang Cao

Abstract

Finite-temperature segregation complicates the screening of multicomponent materials because the operative surface state under annealing conditions can differ substantially from the bulk composition. Using ammonia decomposition reaction (ADR) on Co–Ir–Ni–Rh–Ru as a testbed, we develop a CE–MMC–microkinetic framework for multicomponent materials screening that resolves segregation, lattice strain, and mixed-surface-state retention. A single-descriptor volcano based on *N-hcp adsorption (Δ E (*N)) links temperature-dependent step statistics to activity, while a strain correction enables consistent screening across compositions. MMC simulations predict Ni/Rh surface enrichment and Ru subsurface partitioning, depressing the activity of equimolar alloys after annealing despite high rates in the randomly mixed limit. High-throughput composition-activity maps at 1500 K reveal Ru-rich activity belts with maxima up to ~1.4–1.5× the Ru benchmark, and the surface-configurational-entropy proxy (Δ S conf ) serves as a screening metric for annealing-temperature mixed-surface-state retention. High-〈TOF〉 belts overlap elevated-Δ S conf windows, enabling a two-objective compositional filter that jointly prioritizes ADR activity and mixed-surface-state retention. The resulting maps identify minimal Rh/Ir doping (~5–10%) and substantial Co/Ni substitution (40–60%) as cost-aware design windows, while establishing a computational strategy for annealing-aware screening of multicomponent materials.
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Research context

OpenAlex topics: Ammonia Synthesis and Nitrogen Reduction · Machine Learning in Materials Science · Advanced Materials Characterization Techniques

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