Dynamic Single-Atom Catalysts on Gallium To Overcome the Scaling Relationship Limit: AIMD Screening for CO<sub>2</sub> Reduction and Hydrogen Evolution Reactions
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide Extensive research has been conducted on single-atom catalysts (SACs) for a range of electrochemical reactions. However, static SACs suffer from scaling relationship limits, which hinder their further development. In this work, we introduce the idea of dynamic SACs supported on Gallium for the hydrogen evolution reaction (HER) and the CO 2 reduction reaction (CO 2 RR). We utilized AIMD and DFT calculations to systematically conduct high-throughput screening on s-, p-, d-, and f-block elements supported by Gallium denoted as M-SAC@Ga. We found that among all the understudied catalysts, Re-, Pt-, Pd-, Rh-, Ir-, Au-, Ag-, Ru-, Tc-, Ni-, Cu-, Os-, Hg-, and Ge-SAC@Ga possess thermodynamic and electrochemical stabilities. In addition: Ni-SAC@Ga leads to CO 2 RR overpotentials of 0.28, 0.28, 0.69, and 0.92 V, respectively, toward CHOOH, CO, CH 3 OH, and CH 4 formation. Low overpotentials and mitigation of scaling relationship limits are primarily due to the atomic intelligence (the ability to guide reactions) and dynamic coordination changes of SACs, seen through DFT and AIMD calculations. Analyzing the phonon-induced fluctuations in total energies suggests a standard deviation of up to 0.26 V in the calculated overpotentials. Additionally, the dephasing time for these dynamic systems is below 5 fs, a crucial factor affecting the modeling of catalytic behavior. Feature importance analysis suggests that the d-electron numbers serve as the universal descriptors for these catalysts. This study offers a comprehensive insight into the discovery of cutting-edge electrocatalysts and beyond by applying the concept of dynamic SACs.
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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 | 0.000 | 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) | 0.001 | 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".