Interface Properties of the Partially Oxidized Pt(111) Surface Using Hybrid DFT–Solvation Models
Bibliographic record
Abstract
This article reports a theoretical-computational effort to model the interface between an oxidized platinum surface and aqueous electrolyte. It strives to account for the impact of the electrode potential, formation of surface-bound oxygen species, orientational ordering of near-surface solvent molecules, and metal surface charging on the potential profile along the normal direction. The computational scheme is based on the DFT/ESM-RISM method to simulate the charged Pt(111) surface with varying number of oxygen adatoms in acidic solution. This hybrid solvation method is known to qualitatively reproduce bulk metal properties like the work function. However, the presented calculations reveal that vital interface properties such as the electrostatic potential at the outer Helmholtz plane are highly sensitive to the position of the metal surface slab relative to the DFT-RISM boundary region. Shifting the relative position of the slab also affects the free energy of the system. It follows that there is an optimal distance for the first solvent layer within the ESM-RISM framework, which could be found by optimizing the position of the frozen Pt(111) slab. As it stands, manual sampling of the position of the slab is impractical and betrays the self-consistency of the method. Based on this understanding, we propose the implementation of a free energy optimization scheme of the relative position of the slab in the DFT-RISM boundary region. This optimization scheme could considerably increase the applicability of the hybrid method.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".