Reaction-anchored generative informatics for million-scale CO2 catalyst screening via multi-adsorbate constraints

Abstract

The rational design of electrocatalysts for multi-electron reaction networks hinges on navigating vast, multidimensional surface-environment spaces defined by coupled variations in composition, coordination topology, and adsorbate configurations. Taking electrochemical carbon dioxide reduction to ethanol as a model challenge, traditional screening methodologies struggle to efficiently represent and search these combinatorial landscapes. Here, we present a reaction-constrained generative materials informatics framework anchored by late-stage oxygenated intermediates (*OCH2CH3). A fine-tuned generative Transformer model efficiently sampled 1,000,000 adsorption configurations. To efficiently navigate this massive space, adsorption configurations were compressed into latent topological representations via oxygen-centered local node embeddings derived from the universal models for atoms (UMA) platform. Cluster-guided spatial navigation based on these UMA-derived representations achieved a 15-fold enrichment in positive-candidate discovery over naive representative sampling. By jointly applying multi-adsorbate thermodynamic constraints (*OCH2CH3, *CO, and *H) within the CO2RR network, the workflow down-selected 75 promising material candidates. Density functional theory (DFT) calculations benchmarked representative systems, validating the screening-level fidelity of UMA-predicted binding energies. Furthermore, subgroup discovery extracted interpretable, physically intuitive elemental rules governing candidate enrichment. CO-CO coupling calculations further identified Ga-Pt and Al-Cu-Pd as kinetically favored over Cu. This framework successfully recovers 13 reported CO2RR-related materials while uncovering 62 unexplored compositional domains, providing a transferable paradigm for reaction-constrained generative surface informatics.