Graph neural network-driven prediction of high-performance CO2 reduction catalysts based on Cu-based high-entropy alloys

doi:10.1016/S1872-2067(24)60264-0

Abstract

Abstract:

High-entropy alloy (HEA) offer tunable composition and surface structures, enabling the creation of novel active sites that enhance catalytic performance in renewable energy application. However, the inherent surface complexity and tendency for elemental segregation, which results in discrepancies between bulk and surface compositions, pose challenges for direct investigation via density functional theory. To address this, Monte Carlo simulations combined with molecular dynamics were employed to model surface segregation across a broad range of elements, including Cu, Ag, Au, Pt, Pd, and Al. The analysis revealed a trend in surface segregation propensity following the order Ag > Au > Al > Cu > Pd > Pt. To capture the correlation between surface site characteristics and the free energy of multi-dentate CO₂ reduction intermediates, a graph neural network was designed, where adsorbates were transformed into pseudo-atoms at their centers of mass. This model achieved mean absolute errors of 0.08-0.15 eV for the free energies of C₂ intermediates, enabling precise site activity quantification. Results indicated that increasing the concentration of Cu, Ag, and Al significantly boosts activity for CO and C₂ formation, whereas Au, Pd, and Pt exhibit negative effects. By screening stable composition space, promising HEA bulk compositions for CO, HCOOH, and C₂ products were predicted, offering superior catalytic activity compared to pure Cu catalysts.

Key words: Density functional theory, Machine learning, CO₂ reduction, High entropy alloys, Graph neural network

Zihao Jiao, Chengyi Zhang, Ya Liu, Liejin Guo, Ziyun Wang. Graph neural network-driven prediction of high-performance CO₂ reduction catalysts based on Cu-based high-entropy alloys[J]. Chinese Journal of Catalysis, 2025, 71: 197-207.

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URL: https://www.cjcatal.com/EN/10.1016/S1872-2067(24)60264-0

https://www.cjcatal.com/EN/Y2025/V71/I4/197

Figures/Tables 5

Fig. 1. Stability analysis of HEAs. (a) Elements incorporated within the compositional space of HEAs. (b) Schematic representation of stability and phase formation criteria. (c) Distribution of stability parameters Ω and δ across all possible compositions, with varying colors indicating different VEC values. (d) Correlation between the metal proportion in stable phases and the averaged mixing enthalpy ΔH.

Fig. 2. A comparison between surface and bulk ratios of various elements after the segregation process. (a) The relationship between the surface ratio and bulk ratio for Ag and Au. Each point represents a possible HEA composition for a given bulk ratio of the specified element. The black points indicate the specified element has the highest concentration on the surface for that composition, indicating its dominance in surface segregation. (b) The relationship between the surface ratio and bulk ratio for element pairs.

Fig. 3. The reaction pathway and dataset generation. (a) Reaction pathways and key intermediates of CO2 reduction and HER. (b) Sample structures from the HEA dataset. (c) Multi-atom intermediates are represented as single pseudo-atoms, encoded through one-hot encoding. (d) Neighbor search for each node in the GNN using a 6 ? cutoff radius.

Fig. 4. Overview of GNN model performance. (a) Workflow of the GNN model. (b) Comparison of model performance using COM and COP. (c) Model predictions on the validation set, where the x-axis represents DFT-calculated free energies and the y-axis shows GNN-predicted free energies. (d) Model predictions on the test set. (e) Average adsorption heights of different intermediates, with error bars representing the standard deviation of adsorption heights.

Fig. 5. (a) Quartile analysis of surface activity in HEAs based on bulk elemental composition. (b) Analysis of dominant potential limiting steps for various products in the AQ3 (high-activity) and AQ1 (low-activity) regions. (c) Composition analysis of the 1000 most active adsorption sites in terms of nearest neighboring atoms. (d) Heatmap of the surface average limiting potential across the compositional space of HEAs. The color scale represents the average limiting potential for the C2 product, with lower values indicating more favorable catalytic activity. Specific HEA compositions with particularly low limiting potentials are highlighted.

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Graph neural network-driven prediction of high-performance CO₂ reduction catalysts based on Cu-based high-entropy alloys

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