Multi-Descriptor Design of Ruthenium Catalysts for Durable Acidic Water Oxidation
Bibliographic record
Abstract
Abstract Further improvements in the performance and cost-effectiveness of water electrolyzers are urgently needed to accelerate decarbonization of hydrogen production. Iridium-free oxygen evolution reaction (OER) electrocatalysts are needed that are active and durable under acidic conditions. Here we report Ru0.6Cr0.2Ti0.2O2, identified from a machine-learning aided density functional theory (DFT) model using Pourbaix decomposition energy and metal-oxygen covalency as descriptors for electrochemical stability. To screen the entire space of bimetallic oxides for stability under harsh acidic conditions, we employ a graph convolution neural network to predict the Pourbaix decomposition energy accurately from unrelaxed structures. This was accomplished with an accuracy of 32 meV/atom. Notably, utilizing an optimized hyperbolic tangent activation function and dropout algorithm reduced the prediction error by 90%. Experimentally, the catalyst has an overpotential of 267 mV at 100 mA/cm2, accompanied by 200 hours of operation with an overpotential increase of less than 5 mV. DFT calculations show that adding Ti into the structure increases the metal-oxygen covalency of the system, improving the stability of the mixed-metal-oxide. At the same time, adding Cr lowers the energy barrier of the HOO* formation rate-determining step, thus improving activity compared to RuO2. We investigate structural and chemical changes during the reaction using in situ X-ray absorption spectroscopy and ptychography-scanning transmission X-ray microscopy. These evidence the evolution of a metastable structure compromised of a strong Ti-oxo network and a hydrous Cr-O passivation layer during the reaction – a structure that slows the dissolution of Ru by 20x while simultaneously suppressing lattice oxygen participation by > 60% compared to the case of RuO2.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".