Machine Learning Reveals the Statistical Secret Behind CO-Tolerant High-Entropy Alloy Catalysts

Fuel cells promise a clean energy future, but a tiny molecule has long stood in their way. Carbon monoxide, or CO, is an almost unavoidable trace contaminant in hydrogen produced from hydrocarbons and even in some reformed fuels, and even parts-per-million levels of it can cripple the platinum anodes at the heart of many fuel cell designs. Now, a new computational study published in the journal Ionics offers one of the most statistically rigorous pictures yet of how a complex six-metal alloy can resist this poisoning, and its findings overturn a tempting oversimplification about how high-entropy alloys work.

Researchers Yibo Peng and Caixia Deng, affiliated with Ningbo University and the Ningbo Institute of Materials Technology and Engineering of the Chinese Academy of Sciences, set out to understand why a so-called senary high-entropy alloy containing platinum, ruthenium, nickel, cobalt, iron, and molybdenum shows tolerance to CO during the alkaline hydrogen oxidation reaction. This reaction is the anode-side half of an anion-exchange membrane fuel cell, a technology prized for its potential to use cheaper, non-precious components than conventional proton-exchange devices. The catch is that hydrogen oxidation proceeds sluggishly in alkaline conditions, and platinum-based anodes are acutely vulnerable to poisoning by even trace amounts of carbon monoxide that latch onto active sites and refuse to let go.

High-entropy alloys have emerged as a compelling answer. Unlike traditional alloys with one dominant metal and minor additives, these materials mix five or more elements in roughly equal proportions, producing a crystalline surface where the identity of every atom’s neighbors is essentially random. That randomness means the surface is not a single, uniform catalyst but a vast landscape of distinct local environments, each potentially binding hydrogen or CO differently. The trouble for theorists is obvious: there are astronomically many such environments, and calculating the adsorption energy of a molecule on each one with conventional density functional theory would be computationally prohibitive.

Peng and Deng tackled this challenge with what they call a compositional ensemble framework, a pipeline that fuses geometric machine-learning descriptors, intelligent sampling, high-throughput quantum calculations, and a state-of-the-art graph neural network. First, they used smooth overlap of atomic positions, or SOAP, descriptors to encode each surface site’s local chemical neighborhood in a form a machine can compare. Then, farthest point sampling allowed them to select a diverse, representative subset of configurations from that enormous space, ensuring the training data spanned the full variety of local environments rather than clustering around a few common motifs.

On that curated training set, the team ran high-throughput density functional theory calculations to obtain accurate adsorption energies, and used the results to train EquiformerV2, an equivariant transformer architecture designed to respect the rotational and translational symmetries of three-dimensional atomic systems. The payoff was striking: the trained model predicts CO adsorption energies with a mean absolute error of just 0.090 electron volts. With that level of accuracy in hand, the researchers could do something previously impractical, namely statistically evaluate CO adsorption across 120,000 distinct surface sites of the PtRuNiCoFeMo alloy, building a distribution rather than a handful of anecdotal data points.

The results reveal a subtle and somewhat counterintuitive picture. Compared with the flat platinum (111) surface, the canonical benchmark in this field, the high-entropy alloy does not uniformly weaken CO adsorption across its surface. Instead, the distribution of CO binding energies becomes continuously broadened, stretching from sites that bind CO far more weakly than platinum to sites that grip it even more tightly. The overall CO adsorption distribution remains dominated by intermediate-to-strong binding configurations, particularly at bridge and hollow geometries where the molecule can bond to multiple surface atoms simultaneously. In other words, the average alloy surface is, by and large, still a welcoming host for CO.

Yet buried within that distribution lies the alloy’s real advantage. When the researchers performed a comparative two-dimensional analysis of hydrogen and CO adsorption energies, screening each site against the platinum-referenced criterion of accessible hydrogen binding combined with relatively weakened CO binding, they identified 9,479 sites that met both conditions. That corresponds to roughly 7.9 percent of the examined ensemble. These sites, the authors emphasize, should be interpreted as a minority but statistically resolvable reservoir of local motifs where hydrogen chemistry and CO poisoning are effectively decoupled, rather than as evidence that the entire alloy surface outperforms platinum in CO tolerance. It is a reservoir effect: the catalyst as a whole retains enough clean, hydrogen-friendly real estate to keep working even as other regions succumb to adsorbed CO.

Perhaps the most actionable finding concerns what those favorable sites look like at the atomic scale. The team found that the H/CO-favorable motifs are mainly associated with platinum atoms sitting at top-site positions whose nearest-neighbor shells are enriched in molybdenum and platinum. This suggests that nearby molybdenum-platinum coordination is a prominent local environment for balancing hydrogen accessibility against reduced CO affinity. The result dovetails with decades of experimental observations that molybdenum-containing platinum catalysts, from early PtMo alloys to modern MoOx-Pt composites, exhibit exceptional CO tolerance, often attributed to molybdenum’s oxophilicity and its electronic modifying influence on adjacent platinum atoms. The new work reframes that intuition in statistical terms, pinpointing the specific coordination motif worth engineering.

The methodological significance of the study may prove as durable as its catalytic insights. By demonstrating that SOAP descriptors, farthest point sampling, DFT training data, and an equivariant transformer can be chained into a reliable surrogate model for adsorption energies on chemically disordered surfaces, the authors offer the electrocatalysis community a blueprint for interrogating other high-entropy systems, from oxygen reduction catalysts to CO2 conversion electrodes. The statistical framing itself is a corrective: rather than asking whether a high-entropy alloy binds a poison more weakly than a pure metal on average, designers should ask how large the subpopulation of protective local motifs is, and whether synthetic strategies can enlarge it.

For the fuel cell industry, the implications are tantalizing though still computational. Anion-exchange membrane fuel cells need anodes that combine fast alkaline hydrogen oxidation kinetics with robustness against fuel impurities, and a surface in which nearly eight percent of sites are naturally H/CO-decoupled represents a meaningful margin of tolerance. The study also suggests a concrete design lever: tuning synthesis and annealing conditions to promote molybdenum-enriched neighborhoods around surface platinum atoms could, in principle, expand the favorable reservoir further. As hydrogen energy infrastructure scales up globally, turning statistical portraits of disorder like this one into practical catalyst recipes may become one of the field’s central pursuits, bridging the gap between atomic-scale randomness and real-world device durability.

Subject of Research: Statistical machine-learning analysis of CO tolerance in a PtRuNiCoFeMo high-entropy alloy catalyst for alkaline hydrogen oxidation in fuel cells

Article Title: Statistical origin of CO tolerance during alkaline hydrogen oxidation on a PtRuNiCoFeMo high-entropy alloy

Article References: Peng, Y., & Deng, C. (2026). Statistical origin of CO tolerance during alkaline hydrogen oxidation on a PtRuNiCoFeMo high-entropy alloy. Ionics. https://doi.org/10.1007/s11581-026-07511-1

Image Credits: AI Generated

DOI: 10.1007/s11581-026-07511-1

Keywords: high-entropy alloy, CO tolerance, hydrogen oxidation reaction, anion-exchange membrane fuel cell, PtRuNiCoFeMo, adsorption energy, density functional theory, machine learning, graph neural network, EquiformerV2, electrocatalysis, molybdenum-platinum coordination