Machine Learning Models Enable Large-Scale Simulations of CO Dimerization on Copper Catalysts
Researchers introduced Open Catalyst 2025 (OC25), the largest dataset for solid-liquid interfaces, and used it to simulate CO dimerization on copper surfaces at unprecedented scale. The study ran simulations of over 800 atoms for up to 7 nanoseconds — far beyond what traditional ab initio methods allow — examining how surface charge, cation identity, and surface geometry affect a key step in CO₂ electroreduction. The findings suggest that stepped copper surfaces offer a more favorable reaction pathway, with implications for designing more efficient carbon-capture catalysts.
A team of researchers has released Open Catalyst 2025 (OC25), described as the largest dataset to date for modeling solid-liquid interfaces, and demonstrated its utility by studying CO dimerization on copper surfaces — a critical early step in electrochemical CO₂ reduction. Using machine learning interatomic potentials trained on OC25, the team conducted simulations with cells exceeding 800 atoms and timescales up to 7 nanoseconds, representing the most extensive explicit-solvent CO dimerization study performed so far. The simulations computed free-energy profiles across a range of conditions, varying surface charge density, the identity of cations in solution, and the crystallographic facet of the copper surface. Results indicate that CO dimerization is relatively insensitive to surface charge and cation identity under most conditions, with meaningful stabilization of the dimerization transition state appearing only at the most negative charge densities. Notably, the stepped Cu(310) surface was found to provide a more energetically favorable dimerization pathway at modest reducing potentials compared to flat surfaces. The authors argue that OC25-trained models can serve as scalable, practical tools for electrocatalysis research, enabling simulations orders of magnitude larger than conventional first-principles approaches. The work was posted as a preprint on arXiv and has not yet undergone formal peer review.
What's missing
As a preprint, this work has not yet been peer-reviewed. The study does not report experimental validation of the computed free-energy profiles against electrochemical measurements, leaving open the question of how accurately the OC25-trained models reproduce real-world catalytic behavior. Additionally, the study focuses on a single elementary step and does not address the full CO₂ reduction reaction network.
What different sources said
- arXiv physicsCenter
Insights into CO dimerization at electrified Cu interfaces from large-scale machine learning simulations
Related
Gut Bacteria Enzyme Found to Break Down Heat-Processed Food Compounds, Producing Novel Biogenic Amines
Researchers have discovered that an enzyme in common gut bacteria can degrade N-epsilon-carboxymethyllysine (CML), a compound formed during thermal food processing, producing previously unknown biogenic amines. The enzyme, ornithine decarboxylase SpeC from enterobacteria, acts on CML and related modified lysine derivatives through a low-level 'underground' catalytic activity. This finding suggests a previously unrecognized communication axis between thermally processed dietary compounds and gut microbial physiology, with potential implications for host health.
Full-Length Gene Sequencing Reveals Two Distinct Bacterial Communities in Black-Legged Ticks Expanding Into Canada
Researchers used Oxford Nanopore full-length 16S rRNA gene sequencing to characterize the microbiome of Ixodes scapularis black-legged ticks collected in Nova Scotia, Canada, distinguishing between tick-adapted bacteria and environmentally acquired bacteria. The study comes as I. scapularis — the primary vector of Lyme disease — is rapidly expanding northward into Canada due to climate change. The findings suggest that environmentally derived bacteria in tick microbiomes are not mere contamination, which has implications for how tick microbiome data is collected and interpreted across surveillance studies.
Study Identifies Metabolic Link Between Cell Envelope Stress and Biofilm Formation in Bacteria
Researchers have discovered that the metabolite acetyl-CoA directly inhibits enzymes that degrade the bacterial signaling molecule c-di-GMP, connecting cell envelope biosynthesis stress to biofilm formation in Pseudomonas aeruginosa. The study found that sub-inhibitory concentrations of antibiotics targeting early peptidoglycan biosynthesis — but not other antibiotic classes — elevate c-di-GMP levels by reducing phosphodiesterase activity, with acetyl-CoA competing for the enzyme active site. Because the relevant enzyme domain is broadly conserved across bacterial species, this checkpoint mechanism may be widespread and could have implications for understanding antibiotic-induced biofilm responses.