Automated Machine Learning of Interfacial Interaction Descriptors and Energies in Metal-Catalyzed N<sub>2</sub> and CO<sub>2</sub> Reduction Reactions
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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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".