Arce-Ramos, J. M., Trinh, Q. T., Wong, Z. M., Wang, B., Chen, B. W. J., Zhang, J., Tan, T. L. (2025). Breaking scaling relations in AgAuCuPdPt high-entropy alloy nanoparticles for CO2 electroreduction via machine learning. Materials Horizons, 12(23), 10124–10134. https://doi.org/10.1039/d5mh01064k
Abstract:
CO2 electroreduction 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.
License type:
Attribution-NonCommercial 4.0 International (CC BY-NC 4.0)
Funding Info:
This research / project is supported by the Agency for Science Technology and Research - RIE 2025 Manufacturing, Trade, and Connectivity programmatic fund
Grant Reference no. : M24N4b0034
This research / project is supported by the Singapore National Research Foundation - Competitive Research Program funding
Grant Reference no. : NRF-CRP23-2019-0001
This research / project is supported by the A*STAR, National Research Foundation - RIE2025 USS Low Carbon Energy Research Phase 2 Programme HETFI Directed Hydrogen Programme
Grant Reference no. : U2305D4001
This research is supported by core funding from: Australian Research Council
Grant Reference no. : FL230100023