Machine Learning Assisted Materials Classification to Boost Catalyst Design for Electrochemical Oxidation
Bibliographic record
Abstract
Machine learning (ML) is revolutionizing materials science with electrocatalysis emerging as a particularly promising area. While numerous noble and non-noble materials have been explored for chlorine evolution reactions (CER), identifying robust and readily accessible electrocatalysts for broader electrochemical oxidation remains a significant challenge. In this study, we leverage ML to address this gap and identify such materials. We examine the complex relationships between the Fermi level and conduction band position of cobalt-based oxides, alongside various uncommon descriptors such as formation energy, energy above the hull, density, number of magnetic sites, and total magnetization. The data underwent careful cleaning and feature engineering using different ML processes to ensure accuracy. Models were trained on 70% of the data and tested on the remaining 30%. Using a Random Forest classifier, we analyzed electrochemical data and identified Co 3 O 4 as a cost-effective and scalable material for electrochemical oxidation. This machine-learning-driven approach revealed that Co 3 O 4 is more susceptible to oxidation, leading to high reaction efficiency in synthetic applications such as arene chlorination and epoxide conversion.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".