Naslov
Photoinduced desorption dynamics of CO from Pd(111): a neural network approach

Autori
Serrano-Jiménez, Alfredo ; Muzas, Alberto Sánchez ; Zhang, Yaolong ; Jiang, Bin ; Lončarić, Ivor ; Juaristi, Iñaki ; Alducin, Maite

Vrsta, podvrsta i kategorija rada
Sažeci sa skupova, sažetak, znanstveni

Izvornik
APS March Meeting 2021, Bulletin of the American Physical Society, Volume 66, Number 1 / - : The American Physical Society (APS), 2021

Skup
APS March Meeting 2021

Mjesto i datum
Online, 15.03.2021. - 19.03.2021

Vrsta sudjelovanja
Predavanje

Vrsta recenzije
Recenziran

Ključne riječi
neural network ; molecular dynamics ; desorption

Sažetak
A novel approach based on a neural network (NN)- generated potential energy surface (PES) is developed to describe the dynamics of the femtosecond laser-induced desorption of CO from Pd(111). Using trajectories computed with (Te, Tl) ab-initio molecular dynamics with electronic friction (AIMDEF)1 as input data, the NN-PES is trained within the embedded atom neural network framework using the atomic configurational energies and forces2. The NN-PES robustness is checked by studying the errors in energies and forces, and also by testing its performance in complex molecular dynamics simulations. The (Te, Tl)-AIMDEF results1 are reproduced with a remarkable level of accuracy. This shows the outstanding performance of the obtained NN-PES that can cover an extensive range of surface temperatures (90-1000 K) and a large amount of degrees of freedom -those corresponding to multiple adsorbates and surface atoms. Application of this NN-PES for future computational tests of the system dynamics under different initial conditions should be straightforward, as well as the utilization of this methodological framework for development of accurate NN-PESs for other complex gas-solid interfaces.

Izvorni jezik
Engleski

Znanstvena područja
Fizika

POVEZANOST RADA