Machine Learning Models Enable Large-Scale Simulations of CO Dimerization on Copper Catalysts
Researchers introduced Open Catalyst 2025 (OC25), a machine learning dataset for modeling solid-liquid interfaces, and used it to simulate CO dimerization on copper surfaces—a key step in converting CO₂ to useful chemicals. The study conducted the largest explicit-solvent simulations of this process to date, examining how surface charge, cations, and crystal facets affect the reaction. The findings could accelerate development of more efficient electrocatalysts for carbon dioxide reduction and other energy technologies.
A new machine learning dataset called Open Catalyst 2025 (OC25) enables computational simulations of chemical reactions at solid-liquid interfaces that are orders of magnitude larger and faster than traditional quantum chemistry methods. Researchers used OC25-trained models to investigate CO dimerization on copper surfaces under various conditions, including different surface charges, cation identities, and crystal facets, conducting simulations with over 800 atoms and timescales up to 7 nanoseconds. The results show that dimerization is relatively insensitive to charge and cation identity except at very negative charge densities, while stepped copper surfaces (Cu(310)) offer more favorable reaction pathways at modest reducing potentials. This work demonstrates that machine learning models trained on large interfacial datasets can serve as practical tools for electrocatalysis research, enabling the investigation of complex electrochemical transformations that would be computationally prohibitive using ab initio methods alone. The findings have implications for designing better catalysts for CO₂ electroreduction and other energy conversion technologies.
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- arXiv physicsCenter
Insights into CO dimerization at electrified Cu interfaces from large-scale machine learning simulations
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