Combined High-Throughput DFT and ML Screening of Transition Metal Nitrides for Electrochemical CO<sub>2</sub> Reduction
Bibliographic record
Abstract
The electrochemical reduction of CO 2 (CO 2 RR) using renewable electricity has the potential to reduce atmospheric CO 2 levels while producing valuable chemicals and fuels. However, the practical implementation of this technology is limited by the activity, selectivity, and stability of catalyst materials. In this study, we employ high-throughput density functional theory (DFT) calculations to screen ∼800 transition metal nitrides and identify potential catalysts for CO 2 RR. The stability and activity of the screened materials were thoroughly evaluated via thermodynamic analysis, revealing Co, Cr, and Ti transition metal nitrides as the most promising candidates. Additionally, we conduct a feature importance analysis using machine learning (ML) regression models for binding energy prediction and determine the primary factors influencing the stability of catalysts. We show that the group number of metals has a significant impact on the binding energy of *OH and thus on the stability of the catalysts. We anticipate that this combined approach of high-throughput DFT screening and design strategy derived from ML regression analysis could effectively lead to the discovery of improved energy materials.
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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 | 0.001 |
| 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".