Thermodynamic surface reconstruction governs catalytic behavior in high-entropy alloys

TL;DR

This study demonstrates that thermodynamic surface ordering, rather than homogeneous mixing, is crucial for accurately predicting catalytic behavior in high-entropy alloys, highlighting the importance of surface segregation effects.

Contribution

The paper introduces thermodynamic surface reconstruction modeling to improve catalytic activity predictions in high-entropy alloys, surpassing traditional homogeneous surface assumptions.

Findings

Homogeneous surface models fail to match experimental trends.

Thermodynamically annealed surfaces align better with experimental data.

Surface segregation creates chemically selective interfaces.

Abstract

High-entropy alloys are widely modeled as homogeneously mixed surfaces, yet the validity of this assumption for catalytic prediction remains unclear. Here, we reproduce high-throughput experimental measurements using thermodynamic simulations and show that surface ordering is essential for accurately capturing the compositional activity landscape. Homogeneous surface models fail to reproduce experimentally observed trends and, in some regimes, perform at or below the random-selection baseline. In contrast, thermodynamically annealed surfaces restore meaningful agreement with the experimental activity landscape and substantially improve the recovery of active compositions. Segregation energetics reveal strong surface enrichment of preferred elements, producing chemically selective interfaces that collapse the broad adsorption-energy spectrum of random alloys into a narrower distribution of catalytically favorable sites. By linking predictive error to the degree of short-range order, we identify a validity boundary for homogeneous models and establish the thermodynamically selected surface state as a governing parameter for predictive catalysis in multicomponent alloys.