Photoinduced Desorption Dynamics of CO from Pd(111): A Neural Network Approach
Alfredo Serrano Jiménez, Alberto S. Muzas, Yaolong Zhang, Juraj Ovčar, Bin Jiang, Ivor Lončarić, J. I. Juaristi, M. Alducin
Abstract
High Resolution Image Download MS PowerPoint Slide 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, ( T e, T l )-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 ( T e, T l )-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.