Machine Learning Based Electronic Structure Predictors in Single-Atom Alloys: A Model Study of CO Kink-Site Adsorption across Transition Metal Substrates
Bibliographic record
Abstract
This work reports on a comprehensive analysis of the predictive capacity and underlying physicochemical trends provided by d -band based electronic structure features as applied to single-atom alloys (SAAs). Taking CO adsorption energies at kink sites as a model framework, SAA adsorption trends are examined across a range of substrates with vastly differing intrinsic CO adsorption trends. Through this approach, it is demonstrated that SAA adsorption properties can be highly transferable, often displaying atom-like behavior independent of the host substrate, particularly in groups 6 through 12 of the periodic table. The predictability of such SAA behavior is found, however, to be highly qualitative for single d -band based electronic structure features. Nevertheless, it is shown that predictive capacity can be greatly improved through the creation of a feature space comprised of as few as 8 electronic structure features. Intriguingly, following the framework of Hammer and Nørskov, the machine learning accuracy of d -band based electronic structure features is shown to be sensitive to the atomic configuration diversity present in the training ensemble with model accuracy systematically improving through restrictions in the configurational space. More directly, it is shown that elements to the far left of the transition metal block such as Zr and Hf may exhibit CO binding properties comparable to Cu in the CO 2 reduction reaction. However, impurities from groups 6–10 are demonstrated to overbind in a highly transferable manner in line with established pure substrate trends and are likely to act as unwanted posing species concerning CO and the overall CO 2 reduction reaction. The results of this work broadly lay out the predictive capabilities of d -band features as applied to SAAs, as well as their propensity for exhibiting transferable binding properties among d -band substrates.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".