Specific loss power of magnetic nanoparticles: A machine learning approach

A machine learning approach has been applied to the prediction of magnetic hysteresis properties (coercive field, magnetic remanence, and hysteresis loop area) of magnetic nanoparticles for hyperthermia applications. Trained on a dataset compiled from numerical simulations, a neural network and a random forest were used to predict power losses of nanoparticles as a function of their intrinsic properties (saturation, anisotropy, and size) and mutual magnetic interactions, as well as of application conditions (temperature, frequency, and applied field magnitude), for values of the parameters not represented in the database. The predictive ability of the studied machine learning approaches can provide a valuable tool toward the application of magnetic hyperthermia as a precision medicine therapy tailored to the patient's needs. (C) 2022 Author(s).

Automated summary

In this study, machine learning approaches were used to predict the magnetic hysteresis properties of magnetic nanoparticles for hyperthermia applications. A neural network and a random forest model were trained on a dataset compiled from numerical simulations. The predictive ability of these approaches provides a valuable tool for the precision medicine application of magnetic hyperthermia.