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Femtosecond laser-induced desorption of hydrogen molecules from Ru(0001): A systematic study based on machine-learned potentials

Lindner, Steven; Lončarić, Ivor; Vrček, Lovro; Alducin, Maite; Juaristi, J. Iñaki; Saalfrank, Peter (2023) Femtosecond laser-induced desorption of hydrogen molecules from Ru(0001): A systematic study based on machine-learned potentials. The Journal of Physical Chemistry C . ISSN 1932-7447

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Femtosecond laser-induced dynamics of molecules on metal surfaces can be seamlessly simulated with all nuclear degrees of freedom using ab initio molecular dynamics with electronic friction (AIMDEF) and stochastic forces, which are a function of a time-dependent electronic temperature. This has recently been demonstrated for hot-electron-mediated desorption of hydrogen molecules from a Ru(0001) surface covered with H and D atoms [Juaristi, J. I. Phys. Rev. B 2017, 95, 125439]. Unfortunately, AIMDEF simulations come with a very large computational expense that severely limits statistics and propagation times. To keep ab initio accuracy and allow for better statistical sampling, we have developed a neural network interatomic potential of hydrogen on the Ru(0001) surface based on data from ab initio molecular dynamics simulations of recombinative desorption. Using this potential, we simulated femtosecond laser-induced recombinative desorption using varying unit cells, coverages, laser fluences, and isotope ratios with reliable statistics. As a result, we can systematically study a wide range of these parameters and follow dynamics over longer times than hitherto possible, demonstrating that our methodology is a promising way to realistically simulate femtosecond laser-induced dynamics of molecules on metals. Moreover, we show that previously used cell sizes and propagation times were too small to obtain converged results.

Item Type: Article
Uncontrolled Keywords: machine learning; molecular dynamics; machine learning interatomic potential
Subjects: NATURAL SCIENCES > Physics > Condensed Matter Physics
Divisions: Theoretical Physics Division
Project titleProject leaderProject codeProject type
Povećanje prostorne i vremenske skale modeliranja materijala iz prvih principa pomoću strojnog učenja-ExtMatModelMLIvor LončarićUIP-2020-02-5675HRZZ
Depositing User: Ivor Lončarić
Date Deposited: 28 Jul 2023 09:19
DOI: 10.1021/acs.jpcc.3c02941

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