Development of Fukui Function Based Descriptors for a Machine Learning Study of CO<sub>2</sub>Reduction
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Bibliographic record
Abstract
Developing novel methods that capture chemical properties quickly and with reasonable accuracy has emerged as an attractive way to replace time-consuming density functional theory (DFT) calculations. In this study, we propose a new type of machine learning (ML) enhanced descriptors based on the Fukui function (FF) projected onto the Connolly surface. The FF contains information about the local system’s response to the perturbation and could be used as a descriptor of the chemical properties of a surfaces. We show that the FF, augmented by a general characteristic of the electronic structure of the surface, such as a work function, is well correlated to the mapped adsorption energy of CO. Therefore, this combination might replace the computationally expensive mapping of the adsorption energy of small molecules as an indicator of catalytic activity. Potential extensions of the proposed methodology are briefly discussed.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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) | 0.000 | 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 it