Modeling ultrafast photoinduced dynamics of adsorbates on metals with machine learning potentials

I will present theoretical approaches for modeling the dynamics of molecules on metal surfaces induced by short laser pulses.

Modeling is based on a density functional theory derived potential energy surface and the two-temperature model.

The coupling of the adsorbates to the electronic system is modeled in terms of electronic friction and associated stochastic forces that depend on the electronic temperature obtained from the two-temperature model. Using this methodology we have simulated systems such as H/Ru(0001), O2/Ag(110) [1,2], CO/Ru(0001) [3], and CO/Pd(111) [4,5].

We use machine learning to obtain accurate and fast potential energy surfaces that enable long propagation times, realistic coverages, and dynamics in full degrees of freedom.

[1] Phys. Rev. B 93, 014301 (2016) 10.1103/PhysRevB.93.014301

[2] NIM-B 382, 114 (2016) 10.1016/j.nimb.2016.02.051

[3] Phys. Rev. B 94, 165447 (2016) 10.1103/PhysRevB.94.165447

[4] J. Chem. Theory Comput 17, 8, 4648–4659 (2021) 10.1021/acs.jctc.1c00347

[5] Chemical Physics 558, 111518 (2022) 10.1016/j.chemphys.2022.111518