Machine-learning-accelerated discovery of a σ-channel C-O activation mechanism.

Carbon dioxide (CO2) electroreduction is a promising route for carbon recycling and energy regeneration, with the development of dual-atom catalysts (DACs) being critical to its advancement. Conventional density functional theory (DFT) calculations and machine learning (ML) models based on DFT-derived descriptors are plagued by prohibitive computational time and resource costs. Herein, we extract 20 fundamental chemical descriptors exclusively from structural data and deploy a lightweight ML model to a dataset of 250 880 entries, eliminating the need for 12 544 DFT computations and rapidly identifying 10 high-performance two-dimensional BN-based DACs for CO2 electroreduction. The model's reliability and the catalyst's stability and performance are validated via DFT, ab initio molecular dynamics (AIMD) simulations and literature evidence. Contrary to the classic π-backbonding mechanism that relies on strong CO adsorption to trigger activation, we reveal an unprecedented σ-donation-enabled pathway that achieves efficient CO activation via reduced C-O σ bond order within weak-adsorption regions. This defining σ-channel strategy decouples the intrinsic adsorption-activation coupling of π-backbonding theory, markedly alleviating catalyst poisoning, and further offers a cost-effective, high-efficiency paradigm for high-throughput screening of advanced catalytic materials.