Modeling of magnetic cooling power of manganite-based materials using computational intelligence approach

Abstract

Magnetic refrigeration (MR) technology has been identified as a potential replacement for the gas compression system of refrigeration due to its environmental friendliness and high level of efficiency. This technology utilizes manganite-based materials as magnetic refrigerants due to the dependence of magnetic properties as well as relative cooling power (RCP) of this class of materials on the concentration and nature of the dopants. Quantifying the effect of dopants on RCP of manganite-based materials requires a long experimental procedures and techniques that are costly and time-consuming. In order to circumvent these challenges, we propose a model, based on support vector regression (SVR), which quickly estimates the RCP of doped manganite-based materials with high level of accuracy using crystal lattice constants as descriptors. The accuracy and ease with which the proposed SVR-based model estimates RCP of doped manganite-based materials is very promising and effective in designing MR system of desired RCP.