Automated Machine Learning of Interfacial Interaction Descriptors and Energies in Metal-Catalyzed N<sub>2</sub> and CO<sub>2</sub> Reduction Reactions
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The applications of machine learning (ML) in complex interfacial interactions are hindered by the time-consuming process of manual feature selection and model construction. An automated ML program was implemented with four subsequent steps: data distribution analysis, dimensionality reduction and clustering, feature selection, and model optimization. Without the need of manual intervention, the descriptors of metal charge variance (Δ Q CT ) and electronegativity of substrate (χ sub ) and metal (δχ M ) were raised up with good performance in predicting electrochemical reaction energies for both nitrogen reduction reaction (NRR) and CO 2 reduction reaction (CO 2 RR) on metal–zeolites and MoS 2 surfaces. The important role of interfacial interactions in tuning the catalytic reactivity in NRR and CO 2 RR was highlighted from SHAP analysis. It was proposed that Fe-, Cr-, Zn-, Nb-, and Ta-zeolites are favorable catalysts for NRR, while Ni-zeolite showed a preference for CO 2 RR. An elongated bond of N 2 or a bent configuration of CO 2 was shown in V-, Co-, and Mo-zeolites, indicating that the molecule could be activated after the adsorption in both NRR and CO 2 RR pathways. The generalizability of the automatically built ML model is demonstrated from applications to other catalytic systems such as metal–organic frameworks and SiO 2 surfaces. The automated ML program is a useful tool to accelerate the data-driven exploration of relationship between structures and material properties without the need of manual feature selection.
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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 | 0.001 |
| 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