Neural-network assisted study of nitrogen atom dynamics on amorphous solid water – I. adsorption and desorption
| Autor: | Germán Molpeceres, Viktor Zaverkin, Johannes Kästner |
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| Rok vydání: | 2020 |
| Předmět: |
Physics
Sticking coefficient 010304 chemical physics Binding energy FOS: Physical sciences Astronomy and Astrophysics Computational Physics (physics.comp-ph) Astrophysics - Astrophysics of Galaxies 01 natural sciences 7. Clean energy Amorphous solid Condensed Matter::Materials Science Molecular dynamics Adsorption Space and Planetary Science Chemical physics Astrophysics of Galaxies (astro-ph.GA) Desorption 0103 physical sciences Potential energy surface Diffusion (business) Physics - Computational Physics 010303 astronomy & astrophysics |
| Zdroj: | Monthly Notices of the Royal Astronomical Society. 499:1373-1384 |
| ISSN: | 1365-2966 0035-8711 |
| Popis: | Dynamics of adsorption and desorption of (4S)-N on amorphous solid water are analysed using molecular dynamic simulations. The underlying potential energy surface was provided by machine-learned interatomic potentials. Binding energies confirm the latest available theoretical and experimental results. The nitrogen sticking coefficient is close to unity at dust temperatures of 10 K but decreases at higher temperatures. We estimate a desorption time-scale of 1 μs at 28 K. The estimated time-scale allows chemical processes mediated by diffusion to happen before desorption, even at higher temperatures. We found that the energy dissipation process after a sticking event happens on the picosecond time-scale at dust temperatures of 10 K, even for high energies of the incoming adsorbate. Our approach allows the simulation of large systems for reasonable time-scales at an affordable computational cost and ab initio accuracy. Moreover, it is generally applicable for the study of adsorption dynamics of interstellar radicals on dust surfaces. |
| Databáze: | OpenAIRE |
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