Intelligent Design of Nanoparticles for Photochemical Applications: a Computational Biology Approach using Artificial Intelligence

EasyChair Preprint 14981

Abstract

The design of nanoparticles with tailored properties for photochemical applications poses significant challenges due to the intricate relationships between nanoparticle structure, composition, and optical response. This study explores the integration of computational biology approaches with artificial intelligence (AI) to optimize nanoparticle design for enhanced photochemical performance. By leveraging machine learning algorithms, molecular dynamics simulations, and quantum mechanical modeling, we develop a predictive framework for identifying optimal nanoparticle architectures. Our approach enables the rapid screening of vast nanoparticle design spaces, accelerating the discovery of novel photoactive materials. Results demonstrate substantial improvements in photocatalytic efficiency, photostability, and tunability of nanoparticle optical properties. This work showcases the potential of AI-driven computational biology in revolutionizing the design of nanoparticles for photochemical applications, including solar energy harvesting, photocatalysis, and optoelectronics.

Keyphrases: Artificial Intelligence, Nanoparticles, computational biology, machine learning, photochemical applications

@booklet{EasyChair:14981,
  author    = {Abey Litty},
  title     = {Intelligent Design of Nanoparticles for Photochemical Applications: a Computational Biology Approach using Artificial Intelligence},
  howpublished = {EasyChair Preprint 14981},
  year      = {EasyChair, 2024}}