First-Principles Modeling of Equilibration Dynamics of Hyperthermal Products of Surface Reactions Using Scalable Neural Network Potential

Abstract

Equilibration dynamics of hot oxygen atoms following O2 dissociation on Pd(100) and Pd(111) surfaces are investigated by molecular dynamics simulations based on a scalable neural network potential enabling first-principles description of O2 and O interacting with variable Pd supercells. We find that to accurately describe the equilibration dynamics after dissociation, the simulation cell length necessarily exceeds twice the maximum distance of equilibrated oxygen adsorbates. By analyzing hundreds of trajectories with appropriate initial sampling, the measured distance distribution of equilibrated atom pairs on Pd(111) is well reproduced. However, our results on Pd(100) suggest that the ballistic motion of hot atoms predicted previously is a rare event under ideal conditions, while initial molecular orientation and surface thermal fluctuation could significantly affect the overall post-dissociation dynamics. On both surfaces, dissociated oxygen atoms remain primarily locate their nascent positions and then randomly cross bridge sites nearby.