Data-Driven Exploration of Spinel Ferrite Nanoparticles:From Experimental Studies to Machine Learning Predictions

Abstract

In recent years, spinel ferrite nanoparticles have attracted considerable research interest due to their exceptional magnetic, electrical, and optical properties. Because of these unique characteristics, they have found widespread applications in several fields, including biomedicine, magnetic sensing devices, catalysis, and information storage technologies. Among the different types of spinel ferrites, nickel ferrite (NiFe₂O₄) and cobalt ferrite (CoFe₂O₄) have received special attention because they exhibit excellent magnetic properties, good chemical stability, and a stable crystal structure. This review focuses on recent advancements in both experimental research and machine-learning-assisted studies of spinel ferrite nanoparticles. It discusses the influence of dopant concentration on several important material properties, such as crystal size, optical band gap, saturation magnetization, degree of inversion, and Curie temperature. The review also emphasizes the growing importance of machine learning techniques, including Artificial Neural Networks (ANN), Support Vector Regression (SVR), Gaussian Process Regression (GPR), Random Forest (RF), and XGBoost. These methods are increasingly being used to predict material behavior, analyze complex relationships among different parameters, and optimize the design and performance of ferrite materials.The literature reviewed in this study demonstrates that data-driven approaches can provide reliable and accurate predictions while significantly reducing the need for extensive and time-consuming laboratory experiments. Therefore, the integration of experimental investigations with machine learning techniques represents an efficient and promising strategy for accelerating the development and optimization of advanced spinel ferrite materials for future scientific, technological, and industrial applications.