Machine Learning-Driven Design of Electrodeposited Metal Oxide Interfaces for Alkaline Hydrogen Evolution
Bibliographic record
Abstract
Most catalyst design strategies for the hydrogen evolution reaction (HER), including density functional theory (DFT) and high-throughput screening, rely on idealized metallic surfaces or fully alloyed structures. However, in practical electrochemical systems, especially those operating at room temperature and pressure, transition metals tend to exist as partially oxidized species. Despite their prevalence, metal oxides have received limited attention as tunable and scalable HER catalysts under realistic operating conditions. To address this gap, we employed machine learning-based predictions from the Open Catalyst Project (OCP) to guide the synthesis of HER-active metal oxide interfaces on nickel foam substrates. We focused on transition metals such as Cu, Co, and Ni, all of which are low-cost. Instead of forming homogeneous alloys, we developed a sequential electrodeposition method where a primary metal oxide layer is first deposited, followed by a secondary precursor layer to construct a bilayer structure. This design enables interface tuning without requiring high-temperature treatment or complete alloying, making the system highly reproducible and compatible with mild processing. We integrated the catalyst into a heterogeneous electrolysis setup using a gel-mediated interface, wherein a polyvinyl alcohol (PVA)–based gel electrolyte separated the working and counter electrodes while maintaining ionic conductivity and structural integrity. This configuration decoupled ion transport and gas evolution, allowing the system to adapt to air-fed or semi-wet operation. Even with these promising results, we continue to carry out long-term HER testing and Faradaic efficiency evaluations. Meanwhile, the system architecture and material design were optimized based on computational insights from OCP, including surface reactivity trends and hydrogen adsorption energetics. Here, we demonstrate a machine learning-guided electrodeposition approach for constructing HER-active metal oxide interfaces. By leveraging both computational insights and accessible transition metal oxides (Cu, Co, Ni), this strategy provides a robust platform for scalable alkaline hydrogen production.
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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".