A Study on Prediction of Size and Morphology of Ag Nanoparticles Using Machine Learning Models for Biomedical Applications

Abstract

The synthesis of silver nanoparticles (AgNPs) holds significant promise for various applications in fields ranging from medicine to electronics. Accurately predicting the particle size during synthesis is crucial for optimizing the properties and performance of these nanoparticles. In this study, we compare the efficacy of tree-based models compared with the existing models, for predicting the particle size in silver nanoparticle synthesis. The study investigates the influence of input features, such as reaction parameters, precursor concentrations, etc., on the predictive performance of each model type. Overall, this study contributes to the understanding of modeling techniques for nanoparticle synthesis and underscores the importance of selecting appropriate methodologies for accurate particle size prediction, thereby facilitating the optimization of synthesis processes and enhancing the effectiveness of silver nanoparticle-based applications.