Machine-Learning-Driven High-Entropy Alloy Catalyst Discovery to Circumvent the Scaling Relation for CO<sub>2</sub> Reduction Reaction
Why this work is in the frame
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Bibliographic record
Abstract
To achieve an equitable energy transition toward net-zero 2050 goals, the electrochemical reduction of CO2 (CO2RR) to chemical feedstocks through utilizing both CO2 and renewable energy is particularly attractive. However, the catalytic activity of CO2RR is limited by the scaling relation of the adsorption energies of intermediates. Circumventing the scaling relation is a potential strategy to achieve a breakthrough in catalytic activity. Herein, based on density functional theory (DFT) calculations, we designed a high-entropy alloy (HEA) system of FeCoNiCuMo with high catalytic activity for CO2RR. Machine learning models were developed by considering 1280 adsorption sites to predict the adsorption energies of COOH*, CO*, and CHO*. The scaling relation between the adsorption energies of COOH*, CO*, and CHO* is circumvented by the rotation of COOH* and CHO* on the designed HEA surface, resulting in the outstanding catalytic activity of CO2RR with the limiting potential of 0.29–0.51 V. This work not only accelerates the development of HEA catalysts but also provides an effective strategy to circumvent the scaling relation.
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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 | 0.001 |
| Science and technology studies | 0.002 | 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) | 0.000 | 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