A study of two‐dimensional single atom‐supported <scp>MXenes</scp> as hydrogen evolution reaction catalysts using density functional theory and machine learning
Bibliographic record
Abstract
Abstract Screening promising hydrogen evolution reaction (HER) electrocatalysts for water splitting is crucial for the industrial scalability of sustainable energy storage. As HER catalysts, two‐dimensional (2D) MXenes are promising substitution materials for platinum. Tuning the surface termination and loading a single atom can help to improve the electrocatalytic performance of 2D MXenes. We utilized density functional theory (DFT) calculations to explore the catalyst activity, thermal stability, and dynamic stability of 2D single atom‐loaded MXenes with surface terminations. We demonstrate that 21 uninvestigated 2D single‐atom MXene catalysts, among 264 promising candidates, show an electrocatalytic activity surpassing that of platinum. Among the 21 most promising HER catalysts, 7 (Ti 3 C 2 I 2 Ir, Ti 3 C 2 Br 2 Cu, Ti 3 C 2 Br 2 Pt, Ti 3 C 2 Cl 2 Cu, Ti 3 C 2 Cl 2 Pt, Ti 3 C 2 Se 2 Au, and Ti 3 C 2 Te 2 Nb) are dynamically and thermally stable. Furthermore, machine learning tools predicted the catalyst activity and thermal stability using elemental properties that are easily available in chemical data repositories.
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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 | 0.000 | 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".