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Photoinduced Desorption Dynamics of CO from Pd(111): A Neural Network Approach

Modeling the ultrafast photoinduced dynamics and reactivity of adsorbates on metals requires including the effect of the laser-excited electrons and, in many cases, also the effect of the highly excited surface lattice. Although the recent ab initio molecular dynamics with electronic friction and thermostats, (Te, Tl)- AIMDEF [Alducin, M. ; Phys. Rev. Lett. 2019, 123, 246802], enables such complex modeling, its computational cost may limit its applicability. Here, we use the new embedded atom neural network (EANN) method [Zhang, Y. ; J. Phys. Chem. Lett. 2019, 10, 4962] to develop an accurate and extremely complex potential energy surface (PES) that allows us a detailed and reliable description of the photoinduced desorption of CO from the Pd(111) surface with a coverage of 0.75 monolayer. Molecular dynamics simulations performed on this EANN-PES reproduce the (Te, Tl)- AIMDEF results with a remarkable level of accuracy. This demonstrates the outstanding performance of the obtained EANN-PES that is able to reproduce available density functional theory (DFT) data for an extensive range of surface temperatures (90–1000 K) ; a large number of degrees of freedom, those corresponding to six CO adsorbates and 24 moving surface atoms ; and the varying CO coverage caused by the abundant desorption events.

Density functional theory ; Neural networks

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