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

Serrano Jiménez, Alfredo; Sánchez Muzas, Alberto P.; Zhang, Yaolong; Ovčar, Juraj; Jiang, Bin; Lončarić, Ivor; Juaristi, J. Iñaki; Alducin, Maite (2021) Photoinduced Desorption Dynamics of CO from Pd(111): A Neural Network Approach. Journal of Chemical Theory and Computation, 17 (8). pp. 4648-4659. ISSN 1549-9618

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Abstract

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.

Item Type:	Article
Uncontrolled Keywords:	density functional theory; neural networks
Subjects:	NATURAL SCIENCES NATURAL SCIENCES > Physics NATURAL SCIENCES > Physics > Atomic and Molecular Physics
Divisions:	Theoretical Physics Division
Projects:	Project title Project leader Project code Project type Povećanje prostorne i vremenske skale modeliranja materijala iz prvih principa pomoću strojnog učenja-ExtMatModelML Ivor Lončarić UIP-2020-02-5675 HRZZ
Depositing User:	Virna Brumnić
Date Deposited:	24 Mar 2026 12:59
URI:	http://fulir.irb.hr/id/eprint/11443
DOI:	10.1021/acs.jctc.1c00347

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