Transferring energy from solid surfaces to chemical bonds of adsorbed species is a fundamental process in heterogeneous catalysis. In this process, the energetic electrons or holes can scatter into surface species and heat up vibrational modes of adsorbates. This process involves not only the electronic ground state but also the excited state, and it is nonadiabatic with respect to the nuclear motions[2].<br/>In this talk, we will discuss our most recent development of a machine-learning nonadiabatic molecular dynamics approach that uses predicted forces and electronic friction coefficients by ‘learning from data’. Machine learning algorithms, such as the artificial neural networks[4] can use past trajectories as training datasets for fast and accurate prediction of forces and electronic friction coefficients, thus allows us to perform statistical analysis of many trajectories. We benchmark the approach for activation of oxygen from hollow to bridge because of strong interests in understanding laser-induced chemistry of CO oxidation on metal surfaces. By comparing the Head-Gordon/Tully electronic friction with the Local Density Friction Approximation (LDFA), we found that the LDFA is mode unselective, thus giving identical coefficients along the lateral and vertical directions to surface and no oxygen activation is observed, while the Head-Gordon/Tully friction can differentiate the electron dragging force in vertical and lateral modes and capture the laser-induced oxygen activation on Ru [2].

Laser-induced oxygen activation probed by machine-learning accelerated nonadiabatic molecular dynamics

255th National Meeting (2018)

The official archive and repository for all ACS Meetings & Expositions abstracts and select posters or presentations.