CO organization at ambient pressure on stepped Pt surfaces: first principles modeling accelerated by neural networks
| Autor: | Vaidish Sumaria, Philippe Sautet |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
geography
Materials science geography.geographical_feature_category 02 engineering and technology General Chemistry Edge (geometry) 010402 general chemistry 021001 nanoscience & nanotechnology 01 natural sciences 0104 chemical sciences Catalysis Maxima and minima Chemistry Adsorption Terrace (geology) Chemical physics Potential energy surface Molecule 0210 nano-technology Ambient pressure |
| Zdroj: | Chemical Science, vol 12, iss 47 Chemical Science |
| Popis: | Step and kink sites at Pt surfaces have crucial importance in catalysis. We employ a high dimensional neural network potential (HDNNP) trained using first-principles calculations to determine the adsorption structure of CO under ambient conditions (T = 300 K and P = 1 atm) on these surfaces. To thoroughly explore the potential energy surface (PES), we use a modified basin hopping method. We utilize the explored PES to identify the adsorbate structures and show that under the considered conditions several low free energy structures exist. Under the considered temperature and pressure conditions, the step edge (or kink) is totally occupied by on-top CO molecules. We show that the step structure and the structure of CO molecules on the step dictate the arrangement of CO molecules on the lower terrace. On surfaces with (111) steps, like Pt(553), CO forms quasi-hexagonal structures on the terrace with the top site preferred, with on average two top site CO for one multiply bonded CO, while in contrast surfaces with (100) steps, like Pt(557), present a majority of multiply bonded CO on their terrace. Short terraced surfaces, like Pt(643), with square (100) steps that are broken by kink sites constrain the CO arrangement parallel to the step edge. Overall, this effort provides detailed analysis on the influence of the step edge structure, kink sites, and terrace width on the organization of CO molecules on non-reconstructed stepped surfaces, yielding initial structures for understanding restructuring events driven by CO at high coverages and ambient pressure. A neural network potential trained using first-principles calculations enables to understand the adsorption configurations of carbon monoxide on stepped Pt surfaces at ambient pressure. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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