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

TL;DR

This study uses scalable neural network potentials to simulate the equilibration of hot oxygen atoms on Pd surfaces, revealing the importance of simulation cell size and initial conditions in accurately modeling post-dissociation dynamics.

Contribution

It introduces a scalable neural network potential for first-principles molecular dynamics of surface reactions, enabling detailed analysis of oxygen atom equilibration on Pd surfaces.

Findings

Simulation cell size must exceed twice the maximum equilibrated atom distance.

Ballistic motion of hot atoms is rare under ideal conditions.

Initial molecular orientation and surface temperature significantly influence dynamics.

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.