Artificial Intelligence Design and Synthesis of Nanoparticles and Nanomaterials
Project Identifier: ANR-23-CE09-0011
Funding Agency: Agence Nationale de la Recherche (ANR)
Host Institutions: Laboratoire de Chimie – ÉNS de Lyon, CRPP, LAAS-CNRS
Holder: Glenna L Drisko
Duration: September 2023 – August 2027 (48 months)
Overview
AIM addresses the fabrication and optical deployment of complex core-shell nanoparticles, specifically silicon (Si) cores coated with thin metallic shells (such as Au or Ag) measuring under 10 nm. These advanced structures exhibit magnetic dipole resonances coupled with electric dipole responses, making them promising candidates for cutting-edge optical applications like phase plates, near-perfect absorbers, and anti-reflective coatings.
Research Objectives
Producing such ultra-thin metallic shells and organizing them with high spatial precision presents significant chemical and physical hurdles. AIM tackles these challenges through an integrated workflow combining machine learning, automated continuous-flow synthesis, and advanced nanofabrication:
- AI-Driven Design: Simulating extensive libraries of metasurfaces to train artificial intelligence models capable of predicting the ideal spatial arrangement and spacing of core-shell particles for targeted optical wavefront control.
- Continuous-Flow Synthesis: Applying machine learning strategies to regulate continuous-flow synthesis processes, overcoming the inherent difficulties of producing thin gold and silver shells reproducibly.
- Experimental Implementation: Fabricating the designed metasurfaces by creating specialized wells in a substrate via nano-imprint lithography and placing the core-shell particles precisely using blade-coating techniques.
Impact
By bridging machine learning with materials chemistry and nanofabrication, the AIM project introduces methodological innovations across synthesis pathways, nanostructure generation, and optical property optimization.