Artificial Intelligence Models for Predicting Photocatalytic Efficiency of Nanoparticles

EasyChair Preprint 14812

Abstract

The development of efficient photocatalytic nanoparticles (NPs) for energy and environmental applications is a crucial area of research. However, the experimental approach to optimizing NP design is time-consuming and resource-intensive. This study explores the potential of artificial intelligence (AI) models in predicting the photocatalytic efficiency of NPs. We employed machine learning algorithms to analyze a dataset of NP properties and corresponding photocatalytic activities, identifying key descriptors that influence efficiency. Our results show that AI models can accurately predict photocatalytic performance, enabling rapid screening of NP designs and accelerating the discovery of high-performance materials. This approach has far-reaching implications for the development of sustainable energy solutions and environmental remediation technologies.

Keyphrases: Artificial Intelligence, Photocatalytic nanoparticles, machine learning

@booklet{EasyChair:14812,
  author    = {Abilly Elly},
  title     = {Artificial Intelligence Models for Predicting Photocatalytic Efficiency of Nanoparticles},
  howpublished = {EasyChair Preprint 14812},
  year      = {EasyChair, 2024}}