First-Principles-Based Kinetic Monte Carlo Model of Hydrogen Evolution Reaction under Realistic Conditions: Solvent, Hydrogen Coverage and Electric Field Effects
Bibliographic record
Abstract
The hydrogen evolution reaction (HER) plays an important role in electrocatalytic water splitting. Despite the progress on the development of HER catalysts, the dynamic evolution of HER reaction under realistic electrochemical conditions considering the electric field, solvent, and hydrogen coverage effects is still unclear. In this study, a first-principles-based H surface coverage and potential-dependent kinetic Monte Carlo (KMC) HER model on the Pt (111)/Pt (100) surface is presented. The reaction kinetics and electronic structure analysis of HER on Pt surfaces in the presence of dihydrated proton (H 5 O 2 + ) and H surface coverage is investigated using density functional theory (DFT). The HER KMC model was developed based on the DFT-calculated energetics. The KMC simulation results showed that consideration of H 5 O 2 + species and dynamic evolution of H coverage is essential for accurate description of HER reaction on the Pt catalyst, which fits well with HER polarization data. Moreover, sensitivity analysis shows that HER on Pt (111) is mainly affected by the Tafel step. On the Pt(100) surface, HER is primarily governed by the Heyrovsky pathway. Surface species evolution analysis demonstrates that the high working potential accelerated the formation of [Pt-2H] species, leading to increased H coverage and accelerating the HER process. The predicted weakened H binding strength and increased H coverage at high HER working potential was verified by in situ attenuated total reflection Fourier transformed infrared spectroscopy analysis. Overall, the proposed DFT-KMC model represents the state-of-art dynamic simulation of catalytic HER reaction, providing important insights into the evolution of HER under realistic operation conditions.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".