Discovering Electron‐Sponge Behavior at Organic‐Metal Interfaces for CO ₂ Electroreduction via Machine Learning

Abstract Molecular regulation at organic‐metal interfaces is crucial for C─C coupling in CO 2 electroreduction, directly influencing the formation of multi‐carbon (C 2+ ) products. However, the non‐linear interplay of electronic, spatial, and topological molecular descriptors has hindered the establishment of predictive quantitative structure‐activity relationships (QSAR), limiting mechanistic insight. Herein, we employed an interpretable machine learning (ML)‐QSAR framework to link molecular features with the C─C coupling free energy barrier (ΔG‡) on Cu surfaces, uncovering the dominant role of interfacial “electron‐sponge” behavior. Mechanistically, the modifier molecule donates electrons to Cu, which subsequently redistributes them to *CO/*CHO intermediates and the molecule itself, while also directly stabilizing the intermediates. Shapley Additive Explanations (SHAP) analysis identifies key electronic descriptors, including low minimal local electron affinity (LEA min ), narrow HOMO‐LUMO gap and elevated HOMO energy. These descriptors govern the electron‐sponge mechanism, facilitating the reduction of ΔG‡. As a representative molecule, 3,4‐diaminofurazan (DAF), selected from a library of 5,304 graph‐theory‐derived compounds, incorporates electron‐donating and back‐donating amino and furan‐azole motifs. Experimental validation shows a 1.8‐fold increase in C 2+ Faradaic efficiency, from 42% to 77%, confirming the QSAR framework's effectiveness. This descriptor‐driven approach was further extended to Au and Ag systems, providing a scalable pathway for designing next‐generation electrocatalysts.