High-throughput screening and DFT characterization of bimetallic alloy catalysts for the nitrogen reduction reaction
Bibliographic record
Abstract
DFT calculations to understand the enhanced catalytic activity rooted in the electronic structure of the catalyst. Moreover, charge analysis showed significant charge transfer from Re and Mo to Au in these alloys, resulting in a change of their electronic structure, and improvement of their catalytic activity. Finally, the selectivity of the alloys was investigated by comparing the adsorption free energy of nitrogen and hydrogen adatoms, and the resulting theoretical faradaic efficiency. This work further confirms that alloying is an effective approach to enhance the catalytic activity of transition metals and highlights how machine learning algorithms trained with physically intuitive features of the materials can efficiently screen the chemical space of bimetallic alloys and predict the limiting potential for a reaction such as the NRR over these alloys, thereby reducing the computational cost of alloy catalyst design and providing an affordable path to electrocatalytic materials discovery.
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How this classification was reachedexpand
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 | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".