Breaking scaling relations in AgAuCuPdPt high-entropy alloy nanoparticles for CO2electroreduction via machine learning

Abstract

CO2electroreduction is limited by linear scaling relationships that couple the stabilities of key intermediates (*COOH, *CHO) to CO adsorption, placing pure Cu catalysts at a volcano-plot ceiling of activity and selectivity. Here, we harness the compositional variety of nanosized AgAuCuPdPt high-entropy-alloy (HEA) particles to break these constraints. We trained an ultralight linear-regression surrogate (MAE ≈ 0.10 eV) based on density functional theory (DFT) calculations on CO adsorption configurations to screen millions of Monte-Carlo-generated local environments of a variety of HEA formulations in seconds. Sites with predicted CO adsorption energy in the optimal −0.6 to −0.4 eV window were probed explicitly for *COOH and *CHO adsorption. From this screening, we discovered a family of “special” sites—Au centers with coordination number 8 (CN = 8) neighbored by corner Cu atoms of CN = 6—that stabilize bidentate binding of *COOH and *CHO. This lowers the potential-limiting *CO → *CHO step to ∼0 eV, and decisively breaks the scaling relations between CO* and CHO*. Our two-tier machine-learning + DFT workflow identifies active sites on HEAs that outperform the single-metal volcano limit and provides a transferable roadmap for the rational design of next-generation CO2RR electrocatalysts via tuning of the active site composition.