  Highly energetic electron-hole pairs (hot carriers) formed from plasmon decay
in metallic nanostructures promise sustainable pathways for energy-harvesting
devices. However, efficient collection before thermalization remains an
obstacle for realization of their full energy generating potential. Addressing
this challenge requires detailed understanding of physical processes from
plasmon excitation in metal to their collection in a molecule or a
semiconductor, where atomistic theoretical investigation may be particularly
beneficial. Unfortunately, first-principles theoretical modeling of these
processes is extremely costly, limiting the analysis to systems with a few 100s
of atoms. Recent advances in machine learned interatomic potentials suggest
that dynamics can be accelerated with surrogate models which replace the full
solution of the Schroedinger Equation. Here, we modify an existing neural
network, Hierarchically Interacting Particle Neural Network (HIP-NN), to
predict plasmon dynamics in Ag nanoparticles. We demonstrate the model's
capability to accurately predict plasmon dynamics in large nanoparticles of up
to 561 atoms not present in the training dataset. More importantly, with
machine learning models we gain a speed-up of about 200 times as compared with
the rt-TDDFT calculations when predicting important physical quantities such as
dynamic dipole moments in Ag55 and about 4000 times for extended nanoparticles
that are 10 times larger. This underscores the promise of future machine
learning accelerated electron/nuclear dynamics simulations for understanding
fundamental properties of plasmon-driven hot carrier devices.
