Speaker
Esmée Berger
(Chalmers University of Technology)
Description
Machine learning potentials have recently been used to simulate chemical dynamics under vibrational strong coupling, in order to gain greater insight into the underlying microscopic mechanisms [1]. These machine learning potentials are trained on data from density functional theory and are used to evaluate the forces during molecular dynamics simulations, which facilitates atomistic insight into the effect of a cavity on the chemical dynamics. Here, the training of such machine learning potentials is explained. The approach is illustrated by applying the method to study the dynamics of water under vibrational strong coupling. [1] C. Schäfer, J. Fojt, E. Lindgren, and P. Erhart, J. Am. Chem. Soc. 2024, 146, 8, 5402–5413
Primary author
Esmée Berger
(Chalmers University of Technology)
