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High-entropy alloys (HEAs) offer tunable compositions and surface structures, presenting significant potential for creating novel active sites to enhance CO₂ reduction (CO₂RR) catalysis, a key process for sustainable energy.

However, the inherent surface complexity and the tendency for elemental segregation—leading to discrepancies between bulk and surface compositions—pose significant challenges for rational catalyst design and direct investigation via methods like density functional theory.

A research team led by Liejin Guo (Xi'an Jiaotong University) and Ziyun Wang (University of Auckland) have developed a computational framework to navigate these complexities.

By integrating Monte Carlo/Molecular Dynamics simulations to predict surface segregation with a graph neural network (GNN) to assess site-specific activity, this approach establishes a crucial link between microscopic surface environments and the predicted catalytic performance derived from bulk HEA composition.

The results were in the Chinese Journal of Catalysis.

Their simulations across a range of elements (Cu, Ag, Au, Pt, Pd, Al) revealed a surface segregation propensity order of Ag > Au > Al > Cu > Pd > Pt.

The GNN, innovatively representing adsorbates as pseudo-atoms, accurately predicted intermediate free energies (MAE 0.08–0.15 eV), enabling precise quantification of site-specific activity.

Applying this framework, the findings indicated that increasing Cu, Ag, and Al content significantly boosts activity for CO and C₂ formation, whereas Au, Pd, and Pt exhibit inhibitory effects. Specific compositional influences on HCOOH formation and the competing hydrogen evolution reaction were also identified.

By integrating segregation predictions with GNN-based activity quantification across the stable composition space, the study successfully predicted promising HEA bulk compositions for CO, HCOOH, and C₂ products, offering potentially superior catalytic performance compared to pure Cu.