Machine Learning Models Capture Plasmon Dynamics in Ag Nanoparticles.

ABSTRACT: 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 the 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, preventing a detailed analysis over a large number of potential nanostructures and limiting the analysis to systems with a few 100s of atoms. Recent advances in machi

SUBMITTER: Habib A 

PROVIDER: S-EPMC10165650 | biostudies-literature | 2023 May

REPOSITORIES: biostudies-literature