Machine Learning-Driven Prediction of Electrochemical Promotion in the Reverse Water Gas Shift Reaction
Bibliographic record
Abstract
Electrochemical promotion of catalysis (EPOC) provides an effective and versatile strategy to enhance catalytic activity, selectivity, and stability in the reverse water–gas shift (RWGS) reaction, facilitating efficient CO 2 hydrogenation to syngas under milder conditions. However, predicting EPOC results using novel catalytic materials under diverse conditions remains challenging. This study introduces a machine learning framework to predict electrochemical promotion behavior and the rate enhancement ratios (ρ), i.e., ratio between promoted and unpromoted reaction rate, based on the selected catalyst, reaction, and electrochemical condition descriptors. Several classification and regression models were trained and tested using a data set compiled from previous studies. The best-performing random forest (RF) and extreme gradient boosting (XGB) models were validated with new experimental data collected from systems employing lithium lanthanum titanate (LLTO) solid electrolyte and Pt–ZnO catalysts, achieving an R 2 of 0.97 and a mean squared error (MSE) of 0.01. This data-driven approach is interpretable, generalizable to other catalytic systems, and provides a powerful tool for advancing the development of catalytic materials for EPOC in RWGS reactions.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 | 0.001 |
| 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 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".