GNN-guided discovery of Cu-HEA CO2 reduction catalysts

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.

Recently, a research team led by Liejin Guo (Xi’an Jiaotong University) and Ziyun Wang (University of Auckland) 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 published in Chinese Journal of Catalysis (DOI: 10.1016/S1872-2067(24)60264-0).

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.

About the journal

Chinese Journal of Catalysis is co-sponsored by Dalian Institute of Chemical Physics, Chinese Academy of Sciences and Chinese Chemical Society, and it is currently published by Elsevier group. This monthly journal publishes in English timely contributions of original and rigorously reviewed manuscripts covering all areas of catalysis. The journal publishes Reviews, Accounts, Communications, Articles, Highlights, Perspectives, and Viewpoints of highly scientific values that help understanding and defining of new concepts in both fundamental issues and practical applications of catalysis. Chinese Journal of Catalysis ranks among the top six journals in Applied Chemistry with a current SCI impact factor of 15.7.

At Elsevier http://www.journals.elsevier.com/chinese-journal-of-catalysis

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