Abstract
Fabrication and design of metallic nanoparticles (NPs) has tremendously advanced over the last decades, enabling a variety of their applications. Many of the latter are based on heat delivery - utilizing plasmonic properties of such NPs, where exposure to light activates conducting electrons at the surface and heats the particle, with consequently transferred heat to the (biological, chemical, medical) environment the NP is embedded in. What is often disregarded is that NPs structurally change under such photo-thermal excitations. It is therefore of prime importance to understand the stability and behavior of metallic NPs at elevated and distributed temperature, and devise strategies for their optimized performance under desired conditions. That is the core objective of the present project, focusing on mono- and bimetallic Au and Ag NPs. To achieve this goal, it is first necessary to determine the atomistic structure of the NPs, for which one must go beyond the computationally expensive density-functional theory (DFT) calculations. For that, we will employ machine learning for training the Au and Ag interatomic potentials based on DFT data, towards incrementally sped up yet accurate relaxation of the NP shape and structure. The subsequent iterative coupling of the obtained morphology with spatially varying optical and thermal response is a cutting-edge development, that will enable us to predictively tailor the NPs under heating and light exposure, for any intended purpose.
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